tag:blogger.com,1999:blog-85248538191299836492024-03-05T16:14:57.991-08:00Brain BoxStokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.comBlogger43125tag:blogger.com,1999:blog-8524853819129983649.post-84260318838164069752016-02-04T07:57:00.000-08:002016-02-04T07:58:13.066-08:00Research Briefing: Testing sensory evidence against mnemonic templates<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhbl3zuXA1Qs5E_y830k6qQQV7tqBLE7xnLjyTFo_gyDHOENh8ca0UsomrmOmhlEwyDxJ_pNzctwMD1T8OhDw_M29hTaNuC94rJbZLqTk4OaN2hYUNSVLKcDdJ8iEpzTTGYtj26cp4prmdq/s1600/Myers.jpeg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhbl3zuXA1Qs5E_y830k6qQQV7tqBLE7xnLjyTFo_gyDHOENh8ca0UsomrmOmhlEwyDxJ_pNzctwMD1T8OhDw_M29hTaNuC94rJbZLqTk4OaN2hYUNSVLKcDdJ8iEpzTTGYtj26cp4prmdq/s200/Myers.jpeg" width="133" /></a></div>
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<span lang="EN-US" style="font-family: "helvetica neue"; font-size: 11pt;">In a new study, published in
<a href="http://elifesciences.org/content/4/e09000">eLife</a>, we investigated how </span><span lang="EN-US" style="font-family: "helvetica neue"; font-size: 11pt;">visual search templates are
reactivated to act as input filters for target detection.</span><span lang="EN-US" style="font-family: "helvetica neue"; font-size: 11pt;"> How the brain maintains a template of
the target of your search (your house keys, for example) has been a
much-debated topic in neuroscience for the past 30 years. Previous research has
indicated that neurons specialized for detecting the sought-after object when
it is in view are also pre-activated when we are seeking it. This would mean
that these ‘template’ neurons are active the entire time that we are searching.<o:p></o:p></span></div>
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<a href="http://elifesciences.org/content/elife/4/e09000/F4.large.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="http://elifesciences.org/content/elife/4/e09000/F4.large.jpg" height="400" width="327" /></a><span lang="EN-US" style="font-family: "helvetica neue"; font-size: 11pt;">We recorded brain activity from human volunteers using
<a href="http://the-brain-box.blogspot.co.uk/2015/05/what-does-meg-measure.html">magnetoencephalography</a> (MEG) as they tried to detect when a particular shape
appeared on a computer screen. The patterns of brain activity could be analyzed
to identify the template that observers had in mind, and to trace when it became
active. This revealed that the template was only activated around the time when
a target was likely to appear, after which the activation pattern quickly
subsided again.<o:p></o:p></span></div>
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<span lang="EN-US" style="font-family: "helvetica neue"; font-size: 11pt;">We also found that holding a template in mind largely corresponded
with different MEG patterns to those activated after a stimulus with the same
orientation appeared on a computer screen. This is contrary to the idea that
the same cells are responsible both for maintaining a template and for perceiving
its presence in our surroundings.</span><span lang="EN-US" style="font-family: "helvetica neue"; font-size: 11pt;"> </span><span lang="EN-US" style="font-family: "helvetica neue"; font-size: 11pt;">The brief activation of the template suggests templates may come online just
in time to filter new sensory evidence to detect targets. This mechanism could
be advantageous because it lowers the amount of neural activity (and hence
energy) needed for the task. Although this points to a more efficient way in
which the brain searches for targets, these findings need to be replicated using
other methods and task settings to confirm whether the brain generally uses templates
in this way. For instance, we would like to know more about where in the brain
such a filter may be set up.</span><span style="font-family: "helvetica neue"; font-size: 11pt;"><o:p></o:p></span></div>
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<span lang="EN-US" style="font-family: "helvetica neue"; font-size: 11pt;">Reference: </span></div>
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<span style="font-family: "helvetica neue";"><span style="font-size: 14.6667px;">Myers, N. E., G. Rohenkohl, V. Wyart, M. W. Woolrich, A. C. Nobre and M. G. Stokes (2015). "Testing sensory evidence against mnemonic templates." Elife 4.</span></span></div>
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StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-83067419483485158672015-08-03T06:08:00.002-07:002015-08-10T07:11:13.305-07:00Journal Club: Decoding spatial activity patterns with high temporal resolution<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjs0HD1fKp1zAflU5PLHQGLGlKzClbKcp-3NzpnoYIU01kxLSesJFpxncCmQw-XlUEgDQqgVd8jbIVek9thnazztlkzoWnejYf-J6HpOkY9TsgIu2rsPMD6mOVVpoo3HdEeqqhuxSI_4Qh_/s1600/65cba586-5160-4929-b95d-15cf25193136.jpeg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjs0HD1fKp1zAflU5PLHQGLGlKzClbKcp-3NzpnoYIU01kxLSesJFpxncCmQw-XlUEgDQqgVd8jbIVek9thnazztlkzoWnejYf-J6HpOkY9TsgIu2rsPMD6mOVVpoo3HdEeqqhuxSI_4Qh_/s200/65cba586-5160-4929-b95d-15cf25193136.jpeg" width="148" /></a></div>
by Michael Wolff<br />
<div>
<br />
on: <a href="http://www.sciencedirect.com/science/article/pii/S1053811915006205#">Cichy, Ramirez and Pantazis (2015) Can visual information encoded in cortical columns be decoded from magnetoencephalography data in humans?</a> NeuroImage<br />
<br />
Knowing what information the brain is holding at any given time is an intriguing prospect. It would enable researchers to explore how and where information are processed and formed in the brain, as well as how they guide behaviour.<br />
<br />
A big step towards this possibility was made in 2005 when Kamitani and Tong decoded simple visual grating stimuli in the human brain using functional magnetic resonance imaging (fMRI). The defining new feature of this study was that instead of looking for differences in overall activity levels between conditions (or in this case visual stimuli), they tested the differences in activity patterns across voxels between stimuli. This method is now more generally known as multivariate pattern analysis (<a href="http://www.ncbi.nlm.nih.gov/pubmed/16899397">MVPA</a>). A classifier (usually linear) is trained on a subset of data to discriminate between conditions/stimuli, and then tested on the left-out data. This is repeated many times, and the percentages of correctly labelled test data are reported. Crucially, this process is carried out separately for each participant, as subtle individual differences in activity patterns and cortical folding would be lost when averaged, defeating the purpose of the analysis. MVPA has since revolutionised fMRI research and, in combination with the increased power of computers, has become a widely used technique.</div>
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The differential brain patterns observed by Kamitani and Tong are thought to arise from the orientation columns in the primary visual cortex (V1), discovered by Hubel and Wiesel more than 50 years ago. They showed that columns contain neurons that are excited differentially by visual stimuli of varying orientations. Since these columns are very small (<1 mm) it is surprising that their activity patterns can apparently be picked up by conventional fMRI with about 2-3mm spatial resolution. More surprising still is that even magnetoencephalography (MEG) and electroencephalography (EEG) seem to be able to decode visual information, which are generally considered to have a spatial resolution of several centimetres! How is this possible?<br />
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Critics have raised alternative possible origins of the decodable patterns, which could result in more coarse-level activity patterns (e.g. by global form properties or overrepresentation of specific stimuli), and thus confound the interpretation of decodable patterns in the brain.<br />
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In response to these criticisms, a recent study by Cichy, Ramirez, and Pantazis (2015) investigated to what extent specific confounds could affect decodable patters by systematically changing the properties of presented stimuli. They used MEG as the physiological measure instead of fMRI. This enabled them to explore the time-course of decoding, which can be used to infer at which visual processing stage decodable patterns arise.<br />
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In the first experiment they showed that neither the cardinal bias (over representation of horizontal or vertical gratings) nor the phase of gratings (and thus local luminance) is necessary to reliably decode the stimuli.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6cMzawxBWfrusG9RipT_NS_ZTBUhe3qO-UAsWszUQq7NaKQOMcAoJE4Id-T6zG_s05KJ4LQ0UWDAcUwIBs6xpvBC6F7mdmqtSIGJ00U2jYgYND3nz7P37OW2ntCFjbi6raZFWrlYgmunT/s1600/Cichy1.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="191" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6cMzawxBWfrusG9RipT_NS_ZTBUhe3qO-UAsWszUQq7NaKQOMcAoJE4Id-T6zG_s05KJ4LQ0UWDAcUwIBs6xpvBC6F7mdmqtSIGJ00U2jYgYND3nz7P37OW2ntCFjbi6raZFWrlYgmunT/s400/Cichy1.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Figure 1. <a href="http://ac.els-cdn.com/S1053811915006205/1-s2.0-S1053811915006205-main.pdf?_tid=b6d72e52-39df-11e5-ae1a-00000aacb35e&acdnat=1438607051_bd06f235db407b169e3bb2dca7b81e29">From Cichy et al., in press</a></td></tr>
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As can be seen from the decoding time-course the decodability is significant approximately 50 ms after stimulus presentation and ramps up extremely quickly, peaking at about 100 ms. This time-course alone, which was very similar in the other experiments testing for different possible confounds, suggests that the decodable patterns arise early in the visual processing pathway, probably in V1. <br />
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The other confounds that were tested involved the radial-bias (neural overrepresentation of lines parallel to fixation), the edge effect (gratings could be represented as ellipses elongated in the orientation of the gratings), and global form (where gratings are perceived as coherent tilted objects). None of these biases could fully explain the decodable patterns, casting doubt on the notion of coarse-level driven decoding. Again, how is this possible, when the spatial resolution of MEG should be far too coarse to pick up such small neural differences?<br />
<br />
The authors tested the possibility of decoding neural activity from the orientation columns with MEG more directly. They projected neurophysiologically realistic activity patterns on to the modelled surface of V1 of one subject (A). The distance between each activity node was comparable to the actual size of the orientation columns. The corresponding MEG scalp recordings were obtained by forward modelling (B) and their differences decoded (C and D). The activity patterns could be reliably discriminated across a wide range of signal to noise ratios (SNR) and, most crucially, at the same SNR as in the first experiment.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjN34PR2nSfT_iSYsIcPKglJKkXpKdcNdKE7CVh-0G-zaOAqNWQ5Yz4P1HQV90UjyfylK4lmIW9tUw5WDS0o9iBBLLfRjQgdfwDQIRiOFWF4oKP0GYFjyIUvdHPzVrAsoTS27Zrit1uoRTR/s1600/Cichy2.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="316" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjN34PR2nSfT_iSYsIcPKglJKkXpKdcNdKE7CVh-0G-zaOAqNWQ5Yz4P1HQV90UjyfylK4lmIW9tUw5WDS0o9iBBLLfRjQgdfwDQIRiOFWF4oKP0GYFjyIUvdHPzVrAsoTS27Zrit1uoRTR/s400/Cichy2.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: 12.8000001907349px;">Figure 2. </span><a href="http://ac.els-cdn.com/S1053811915006205/1-s2.0-S1053811915006205-main.pdf?_tid=b6d72e52-39df-11e5-ae1a-00000aacb35e&acdnat=1438607051_bd06f235db407b169e3bb2dca7b81e29" style="font-size: 12.8000001907349px;">From Cichy et al., in press</a></td></tr>
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This procedure nicely demonstrates the theoretical feasibility of discriminating neural activity at V1 with MEG, and suggests that the well-known “inverse-problem” inherent to MEG and EEG source localisation does not necessarily mean that small activation differences on the sub-millimetre scale are not present in the activation topographies. While it remains impossible to say where the origin of a neural activation pattern lies, the activation pattern of MEG is still spatially rich. <br />
<br />
Even with EEG it is possible to decode the orientations of gratings (Wolff, Ding, Myers, & Stokes, in press); and this can be observed more than 1.5 seconds after stimulus presentation. We believe that there is a bright future ahead for EEG and MEG decoding research: not only is EEG considerably cheaper than fMRI, but the time-resolved decoding offered by both methods could nicely complement the more spatially resolved decoding of fMRI.<br />
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References<br />
<br />
Cichy, R. M., & Pantazis, D. (in press). Can visual information encoded in cortical columns be decoded from magnetoencephalography data in humans? <a href="http://www.sciencedirect.com/science/article/pii/S1053811915006205">NeuroImage</a>.<br />
<br />
Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single neurones in the cat's striate cortex. The Journal of physiology, 148(3), 574-591.<br />
<br />
Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8(5), 679-685.<br />
<br />
Wolff, M. J., Ding, J., Myers, N. E., & Stokes, M. G. (in press). Revealing hidden states in visual working memory using EEG. Frontiers in Systems Neuroscience.</div>
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StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-43080175247797911642015-06-01T07:17:00.000-07:002015-06-01T07:20:38.399-07:00Research Briefing: reward-guided working memory<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtfUReK-h-hqwkk2Mf7d3UdBVTqovI8jRyk4yZ-8aTnLIq50jRDMw-7uERfb-Ob0KfdPE0hRIs42EVQOkkGuD2L0Epc_9fu6jWHL5YYijSh3ZQleehktT9rB8D0Qo6lvTflyxMZI5vrr0s/s1600/George+Wallis.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtfUReK-h-hqwkk2Mf7d3UdBVTqovI8jRyk4yZ-8aTnLIq50jRDMw-7uERfb-Ob0KfdPE0hRIs42EVQOkkGuD2L0Epc_9fu6jWHL5YYijSh3ZQleehktT9rB8D0Qo6lvTflyxMZI5vrr0s/s200/George+Wallis.jpg" width="192" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">George Wallis</td></tr>
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<span lang="EN-US"><span style="background-color: white; font-family: 'Helvetica Neue'; font-size: 14.6666669845581px; line-height: 20.533332824707px;">Research Briefing, by George Wallis</span></span><br />
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In almost any situation, there are hundreds
of things (or ‘stimuli’) that could attract our attention - just count the
number of objects you can see from where you are now. In order to get on with life and avoid total mental
chaos, <a href="mailto:https://www.youtube.com/watch%3Fv=VkrrVozZR2c">we have
to be extremely selective about what we process</a> – most stimuli are
essentially ignored. It is a
long-established finding that the number of things we can hold in mind (or hold
in ‘working memory’) is really very small – about 2-8 depending on the
experiment we run. Clearly we possess
powerful mechanisms that let us filter in only certain stimuli. The ways in which we can select what gets
into working memory is a much-studied topic in psychology, and often
psychologists run experiments in which they present ‘cues’ (e.g. arrows) that
tell people which items in the experiment to ‘select’. This is the experimental equivalent of
pointing out something with your finger.</div>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgRGX8PDhfA96ukhEoGFIbi6dG_UI_-TA0xmV6yLXTPIiY3-nLCwiWRV6WAHipaMkvovTJoVBgGbnKp4Sns46temAUJcdEtA4zsQ801ww2u20jaKs71zU3vYneuZfoAdC23xcOCRET8zzzv/s1600/Harry+Styles.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgRGX8PDhfA96ukhEoGFIbi6dG_UI_-TA0xmV6yLXTPIiY3-nLCwiWRV6WAHipaMkvovTJoVBgGbnKp4Sns46temAUJcdEtA4zsQ801ww2u20jaKs71zU3vYneuZfoAdC23xcOCRET8zzzv/s200/Harry+Styles.png" width="166" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i style="font-size: medium;"><span lang="EN-US">Harry Styles of One Direction <br />giving an attentional cue to the crowd</span></i></td></tr>
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<span lang="EN-US">However, most of the time, this isn’t how
we select what gets into working memory: in the real world people aren’t on
hand to continuously tell us what to pay attention to. One real-world factor psychologists think may
be important in determining whether an item gets into memory is its ‘reward
value’. For example, a twenty-pound note
is more likely to grab our attention than a piece of scrap paper, even if they
are about the same size and appearance.
Our paper recently published in <a href="http://www.tandfonline.com/doi/full/10.1080/13506285.2015.1013168#.VWxl6M9Viko">Visual Cognition</a> </span><!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN PAPERS2_CITATIONS
<citation><uuid>E40C1B47-340F-4740-8087-8B745184948B</uuid><priority>0</priority><publications><publication><volume>23</volume><publication_date>99201503091200000000222000</publication_date><number>1-2</number><doi>10.1080/13506285.2015.1013168</doi><startpage>291</startpage><title>Reward
boosts working memory encoding over a brief temporal
window</title><uuid>E77B07C6-5DF4-4D21-BEB9-0F187A9424A8</uuid><subtype>400</subtype><endpage>312</endpage><type>400</type><url>http://www.tandfonline.com/doi/full/10.1080/13506285.2015.1013168</url><bundle><publication><publisher>
Taylor &amp; Francis Group </publisher><title>Visual
Cognition</title><type>-100</type><subtype>-100</subtype><uuid>126B85C0-3B90-400C-9277-15A459576E0E</uuid></publication></bundle><authors><author><firstName>George</firstName><lastName>Wallis</lastName></author><author><firstName>Mark</firstName><middleNames>G</middleNames><lastName>Stokes</lastName></author><author><firstName>Craig</firstName><lastName>Arnold</lastName></author><author><firstName>Anna</firstName><middleNames>C</middleNames><lastName>Nobre</lastName></author></authors></publication></publications><cites></cites></citation><span
style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US">(Wallis, Stokes, Arnold, & Nobre, 2015)</span><!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-end'></span></span><![endif]--><span lang="EN-US"> describe the results of two experiments we ran in which we looked
at how reward value affects the likelihood that a stimulus will get into
working memory.<o:p></o:p></span></div>
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<a href="https://www.blogger.com/blogger.g?blogID=8524853819129983649" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://www.blogger.com/blogger.g?blogID=8524853819129983649" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://www.blogger.com/blogger.g?blogID=8524853819129983649" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><span lang="EN-US">Anderson and colleagues performed experiments
showing that items displayed in colours that the experimenters had previously
associated with a high monetary reward ‘grab’ attention as people look around a
visual scene for a particular target, slowing them down slightly </span><!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN PAPERS2_CITATIONS
<citation><uuid>51B440D2-F9B8-4CB7-BD05-45AF0686EF0A</uuid><priority>1</priority><publications><publication><volume>108</volume><publication_date>99201106211200000000222000</publication_date><number>25</number><doi>10.1073/pnas.1104047108</doi><startpage>10367</startpage><title>Value-driven
attentional
capture</title><uuid>A6896E03-E61C-42D8-9464-2AB0EEA2FBE7</uuid><subtype>400</subtype><endpage>10371</endpage><type>400</type><url>http://www.pnas.org/cgi/doi/10.1073/pnas.1104047108</url><bundle><publication><url>http://www.pnas.org/</url><title>Proceedings
of the National Academy of
Sciences</title><type>-100</type><subtype>-100</subtype><uuid>A0FC4909-7F85-457B-A306-AA74EAB609F0</uuid></publication></bundle><authors><author><firstName>B</firstName><middleNames>A</middleNames><lastName>Anderson</lastName></author><author><firstName>P</firstName><middleNames>A</middleNames><lastName>Laurent</lastName></author><author><firstName>S</firstName><lastName>Yantis</lastName></author></authors></publication></publications><cites><cite><prefix>e.g.</prefix></cite></cites></citation><span
style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US">(e.g. <a href="http://www.pnas.org/content/108/25/10367.abstract">Anderson, Laurent, & Yantis, </a>2011)</span><!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-end'></span></span><![endif]--><span lang="EN-US">. We adapted their experiment
to look specifically at memory, presenting four nonsense-shapes briefly, and
then testing how well people remembered which shapes had been presented a few
seconds later. Before running the memory
task we associated some of the shapes with high reward and some with low reward. <o:p></o:p></span></div>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEibqu0himQO7bgpOw6G3xhpHfDo8fwUGIa6w4_D3tBdGBsL0VRb4XELTx-MoHZXxZnxWxyYUG1LclCWq2sIzCzJVd-Q1MsTnHGITwEP3rTTd8dbilRHYpALaQ04Vt35oQZOBZFyXTTi2W7y/s1600/task+design.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="146" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEibqu0himQO7bgpOw6G3xhpHfDo8fwUGIa6w4_D3tBdGBsL0VRb4XELTx-MoHZXxZnxWxyYUG1LclCWq2sIzCzJVd-Q1MsTnHGITwEP3rTTd8dbilRHYpALaQ04Vt35oQZOBZFyXTTi2W7y/s320/task+design.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i style="font-size: medium;"><span lang="EN-US">An experimental trial from our shapes experiment</span></i></td></tr>
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<span lang="EN-US">When we asked people to remember four shapes
out of which some items were worth more than others, people didn’t remember the
high value items any better than the low value items. This was a surprise! On the basis of the paper by Anderson, we
expected the high value items would be better remembered – after all, they
ought to grab attention. However, we did
find a curious effect. If all of the
shapes in a display were high-value (a ‘high value trial’), then any one of
them was remembered better than if all the shapes were low value (a ‘low value
trial’). More curious yet, if half the
shapes were high value, and half were low value, any shape was remembered about
equally well – but they were remembered a little less well than when all the
items were high value, and a little better than when all the items were low
value.<o:p></o:p></span></div>
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<span lang="EN-US">We reasoned that this could have been because
people simply made more effort when the shapes in the memory array were higher
in value, on average, and so they did a bit better. However, a more specific (and interesting)
explanation was also possible. We know that a chemical in the brain, <a href="mailto:http://en.wikipedia.org/wiki/Dopamine">dopamine</a>, is involved
in processing reward – in studies on monkeys, where the dopamine neurons are
measured directly, experimenters see ‘pulses’ of dopamine release when rewarded
items are presented to monkeys. We also
know that the <a href="mailto:http://en.wikipedia.org/wiki/Prefrontal_cortex">prefrontal
cortex (PFC)</a> – the part of the brain thought to be most important in
controlling working memory, is ‘soaked’ in dopamine: dopamine is released
throughout the PFC. Some have suggested
that the dopamine pulses ‘open the gate’ to working memory, and the more
dopamine released at a given time, the wider the gate is opened </span><!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN PAPERS2_CITATIONS <citation><uuid>CE19447E-32F4-403A-99A1-E292F9A05D81</uuid><priority>2</priority><publications><publication><publication_date>99200000001200000000200000</publication_date><startpage>713</startpage><title>On
the control of control: The role of dopamine in regulating prefrontal function
and working
memory</title><uuid>F5373AE1-1216-4233-A7BE-CFB87F4B1876</uuid><subtype>400</subtype><endpage>737</endpage><type>400</type><url>http://ccpweb.wustl.edu/pdfs/Chapter31.pdf</url><bundle><publication><title>Control
of cognitive processes: Attention and performance
XVIII</title><type>-100</type><subtype>-100</subtype><uuid>C77A2424-F112-4C49-A4A3-14992BC389B1</uuid></publication></bundle><authors><author><firstName>Todd</firstName><middleNames>S</middleNames><lastName>Braver</lastName></author><author><firstName>Jonathan</firstName><middleNames>D</middleNames><lastName>Cohen</lastName></author></authors></publication></publications><cites></cites></citation><span
style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US">(<a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.319.9438&rep=rep1&type=pdf">Braver & Cohen, 2000</a>)</span><!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-end'></span></span><![endif]--><span lang="EN-US">.<o:p></o:p></span></div>
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<span lang="EN-US">We couldn’t record dopamine firing in our
volunteers, so we couldn’t test this possibility directly. However, it made us wonder – what would
happen if instead of showing our memory items all at the same time, we
presented them very quickly one after the other, in a row? If subjects simply made more effort on those
trials where they had encountered a higher value item, then we would still
expect all of the items we showed to benefit from this. However, we know that dopamine pulses are
only a fraction of a second long (about a third of a second). If we presented each item for about this
length of time, one after the other, then a ‘reward pulse’ might be able to
‘pick out’ the high reward item, and not the other less valuable items. So, we
ran the experiment, with a few adaptations: rather than shapes, we used
coloured lines, presented one after the other, and asked people to remember the
orientation of the lines. Certain
colours were given high or low reward values.<o:p></o:p></span></div>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhWiXjRW66bZg2gULiSIlfIs5m1sfqg_9XkqmiKT3B-OJWjJBmTl-80X1uhph07jojWI3zKhGVGqgmdENaaIumMaAwEd2kq6aEeYS6Nn9aMrsY0rZcKiHtEpMnpb-hDsv7uefmVA7PNKxx1/s1600/task+design+2.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="291" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhWiXjRW66bZg2gULiSIlfIs5m1sfqg_9XkqmiKT3B-OJWjJBmTl-80X1uhph07jojWI3zKhGVGqgmdENaaIumMaAwEd2kq6aEeYS6Nn9aMrsY0rZcKiHtEpMnpb-hDsv7uefmVA7PNKxx1/s320/task+design+2.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i style="font-size: medium;"><span lang="EN-US">An experimental trial from our </span></i><br />
<i style="font-size: medium;"><span lang="EN-US">second, sequential experiment</span></i></td></tr>
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<span lang="EN-US">We found that indeed, only the high-value
item in this experiment was more likely to be encoded, and not its
near-neighbours. This doesn’t prove that
dopamine pulses are responsible – we didn’t measure our volunteers’ dopamine
neurons – but it does suggest that the reward effect is quite tightly localized
in time: a ‘pulse’ tied to an item, not a more general ‘making an effort’
effect. This was an intriguing finding
and it opens up several questions.
Firstly, is this effect really down to dopamine, like we speculate? To find out, we’d need to see what dopamine
neurons are doing at the same time as running the task. Interestingly, there is some evidence that
the diameter of people’s pupils responds rapidly to dopamine release, so maybe
measuring pupil diameter would be a way of getting some more evidence without
having to get inside the brain. <o:p></o:p></span></div>
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<span lang="EN-US">Secondly – what’s the point of this rather
weird-seeming ‘pulse’ mechanism? And why
would it be useful – like in our first experiment – for unrewarded items to get
‘caught by the pulse’? Our speculative
answer to this question is that our experiment was unnatural – we asked our
volunteers to keep staring at the centre of the screen and flashed up the
shapes all together, just for a moment.
They had no chance to move their eyes (indeed we deliberately tried to
prevent that!). However, in more natural
settings, our eyes constantly flit around the scene. This is pretty hard to notice in yourself but
watch a friend’s eyes for a while (without freaking them out too much) – they
jump from place to place continually, ‘fixating’ first this object, then
that. In fact they move about 3 or 4
times per second, jumping from looking at one item to another. If our putative working-memory-updating
pulses were ‘tied’ to these fixations this might provide a mechanism by which
the more rewarded items in the scene were more likely to enter memory.<o:p></o:p></span></div>
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<span lang="EN-US"> <o:p></o:p></span></div>
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References:<br />
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<!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN PAPERS2_CITATIONS
<papers2_bibliography/><span style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US">Anderson, B. A., Laurent, P. A., & Yantis, S.
(2011). Value-driven attentional capture. <i>Proceedings of the National
Academy of Sciences</i>, <i>108</i>(25), 10367–10371.
doi:10.1073/pnas.1104047108<o:p></o:p></span></div>
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<span lang="EN-US">Braver, T. S., & Cohen, J. D. (2000). On the control of control:
The role of dopamine in regulating prefrontal function and working memory. <i>Control
of Cognitive Processes: Attention and Performance XVIII</i>, 713–737.<o:p></o:p></span></div>
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<span lang="EN-US" style="mso-ascii-font-family: Cambria; mso-bidi-font-family: Cambria; mso-hansi-font-family: Cambria;">Wallis, G., Stokes, M. G., Arnold, C., & Nobre, A. C. (2015).
Reward boosts working memory encoding over a brief temporal window. <i>Visual
Cognition</i>, <i>23</i>(1-2), 291–312. doi:10.1080/13506285.2015.1013168</span><br />
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<span lang="EN-US" style="mso-ascii-font-family: Cambria; mso-bidi-font-family: Cambria; mso-hansi-font-family: Cambria;"><o:p></o:p></span></div>
StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-29576368093261109452015-05-31T04:16:00.000-07:002015-05-31T05:24:20.363-07:00What does MEG measure?<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhvRQ8FkXT7xixn90DOffa3gJFBAFaBPDGONhisGGjDU8vUerDxQ78u1Z3_6A4HL51pjIbkthZ04go-5IQbnL3-zK7YEyapQqA_CzARmk_qHXYfr6g9bTCgkVruc6rgc36TgOE4ulM9ltz4/s1600/Lev.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhvRQ8FkXT7xixn90DOffa3gJFBAFaBPDGONhisGGjDU8vUerDxQ78u1Z3_6A4HL51pjIbkthZ04go-5IQbnL3-zK7YEyapQqA_CzARmk_qHXYfr6g9bTCgkVruc6rgc36TgOE4ulM9ltz4/s200/Lev.png" width="142" /></a></div>
<em>This is a guest post by </em><a href="http://www.ohba.ox.ac.uk/team/ResearchStudents/lev-tankelevitch" mce_href="http://www.ohba.ox.ac.uk/team/ResearchStudents/lev-tankelevitch" target="_blank"><em>Lev Tankelevitch</em></a><em>, one of my PhD students. He is currently using MEG to explore </em><a href="http://www.ohba.ox.ac.uk/groups/AttentionGroup/test-1/reward-guided-attention" mce_href="http://www.ohba.ox.ac.uk/groups/AttentionGroup/test-1/reward-guided-attention" target="_blank"><em>reward-guided attention </em></a><em>at the </em><a href="http://www.ohba.ox.ac.uk/" mce_href="http://www.ohba.ox.ac.uk/" target="_blank"><em>Oxford Centre for Human Brain activity</em></a><em>. This article is also cross-posted at the <a href="http://www.nature.com/scitable/blog/brain-metrics/what_does_meg_measure">Brain Metrics</a>.</em><br />
<em><br /></em>
<em><br /></em>
In 1935, <a href="http://en.wikipedia.org/wiki/Hans_Berger" target="_blank">Hans Berger</a> writes in one of his seminal reports on the <a href="http://www.scholarpedia.org/article/Electroencephalogram" target="_blank">electroencephalogram</a> (EEG), addressing the controversy surrounding the origin of the then unbelievable electrical potentials recorded by him from the human scalp:<em><br /></em>
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<tr><td style="text-align: center;"><a href="http://www.nature.com/scitable/content/ne0000/ne0000/ne0000/ne0000/134762531/berger_waves_2_1.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="http://www.nature.com/scitable/content/ne0000/ne0000/ne0000/ne0000/134762531/berger_waves_2_1.jpg" height="128" title="Fig. 1. Hans Berger and his early EEG recordings from the 1930s. Adapted from Wiki Commons." width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: xx-small; text-align: start;">Fig. 1. Hans Berger and his early EEG recordings <br />from the 1930s. Adapted from Wiki Commons.</span></td></tr>
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<blockquote class="tr_bq">
"I disagree with the statement of the English investigators that the EEG originates exclusively in the occipital lobe. The EEG originates everywhere in the cerebral cortex...In the EEG a fundamental function of the human cerebrum intimately connected with the psychophysical processes becomes visible manifest." (see <a href="http://www.tandfonline.com/doi/abs/10.1300/J184v03n02_01#.VWn19BcUokg" target="_blank">here</a> for a history of Hans Berger and the EEG)</blockquote>
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<tr><td style="text-align: center;"><img src="http://www.nature.com/scitable/content/ne0000/ne0000/ne0000/ne0000/134762900/problem_2_1.jpg" height="110" style="margin-left: auto; margin-right: auto;" title="Fig. 2. The forward and inverse problems" width="200" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: xx-small; text-align: start;">Fig. 2. The forward and inverse problems</span></td></tr>
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<br />
Decades later, the correctness of his position is both a blessing and a curse - we now know that the entire brain produces EEG signals, but it has been a struggle to match components of the EEG to their specific sources in the brain, and thus to further our understanding of how exactly the functioning of the brain relates to those psychophysical processes with which Berger was so enthralled. This struggle is best summarised as an <a href="http://en.wikipedia.org/wiki/Inverse_problem" target="_blank"><em>inverse problem</em></a>, in which one begins with a set of observations (e.g., EEG signals) and has to work backwards to try to calculate what caused them (e.g., neural activity in a specific brain region). A massive obstacle to this approach is the fact that as electrical signals pass from the brain to the scalp they become heavily distorted by the skull. This distortion makes it exceedingly difficult to try to <a href="http://www.scholarpedia.org/article/Source_localization" target="_blank">reconstruct the underlying sources</a> in the brain.<br />
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In 1969, the journey to understand the electrical potentials of the brain took an interesting and fruitful detour when David Cohen, a physicist working at MIT, became the first to confidently measure the incredibly tiny magnetic fields produced by the heart's electrical signals (see <a href="http://video.mit.edu/watch/2012-mcgovern-institute-symposium-david-cohen-11235/" target="_blank">here</a> for a talk by David Cohen on the origins of MEG). To do this, he constructed a shielded room, blocking interference from the overwhelming magnetic fields generated by earth itself and by other electrical devices in the vicinity, effectively closing the door on a cacophony of voices to carefully listen to a slight </div>
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<tr><td style="text-align: center;"><a href="http://www.nature.com/scitable/content/ne0000/ne0000/ne0000/ne0000/134762786/comparison_2_1.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="http://www.nature.com/scitable/content/ne0000/ne0000/ne0000/ne0000/134762786/comparison_2_1.jpg" height="272" title="Fig. 3. Comparisons of magnetic field strengths on a logarithmic scale. From Vrba (2002)." width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: xx-small; text-align: start;">Fig. 3. Comparisons of magnetic field strengths <br />on a logarithmic scale. From Vrba (2002).</span></td></tr>
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whisper. His shielding technique became central to the advent of <em>magnetoencephalography </em>(MEG), which measures the yet even quieter magnetic fields generated by the brain's electrical activity. <br />
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This approach to record the brain's magnetic fields, rather than the electrical potentials themselves, was advanced even further by <a href="http://spectrum.ieee.org/biomedical/imaging/how-the-ford-motor-co-invented-the-squid" target="_blank">James Zimmerman and others working at the Ford Motor Company</a>, where they developed the <a href="http://en.wikipedia.org/wiki/SQUID" target="_blank">SQUID</a>, a superconducting quantum interference device. A SQUID is an extremely sensitive magnetometer, operating on the principles of quantum physics beyond the scope of this article, which is able to detect precisely those very tiny magnetic fields produced by the brain. To appreciate the contributions of magnetic shielding and SQUIDs to magnetoencephalography, consider that the earth's magnetic field, the one acting on your compass needle, is at least 200 million times the strength of the fields generated by your brain trying to read that very same<br />
compass.</div>
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<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: xx-small; text-align: start;">Fig. 4. A participant being scanned inside a MEG scanner. <br />From OHBA.</span></td></tr>
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A MEG scanner is a large machine allowing participants to sit upright. As its centrepiece, it contains a helmet populated with many hidden SQUIDs cooled at all times by liquid helium. Typical scanners contain about 300 sensors covering the entirety of the scalp. These sensors include <em>magnetometers</em>, which measure magnetic fields directly, and <a href="http://en.wikipedia.org/wiki/Gradiometer" target="_blank"><em>gradiometers</em></a>, which are pairs of magnetometers placed at a small distance from each other, measuring the difference in magnetic field between their two locations (hence "gradient" in the name). This difference measure subtracts out large and distant sources of magnetic noise (such as earth's magnetic field), while remaining sensitive to local sources of magnetic fields (such as those emanating from the brain). Due to their positioning, magnetometers and gradiometers also provide complementary information about the direction of magnetic fields.<br />
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Given that these magnetic fields occur simultaneously with electrical activity, MEG is afforded the same millisecond resolution as EEG, allowing one to examine neural activity at its natural temporal resolution. This is in contrast to <em>functional magnetic resonance imaging</em>, fMRI, which, using magnetic fields as a tool rather than a target of measurement, actually measures changes in blood oxygenation which occur on the order of seconds, making it impossible to effective pinpoint the time of neural activity (see <a href="http://www.nature.com/scitable/blog/brain-metrics/what_does_fmri_measure">here</a>). Another advantage over fMRI is the fact that electromagnetic signals are more directly related to the underlying neural activity than the <a href="http://en.wikipedia.org/wiki/Haemodynamic_response" target="_blank">haemodynamic response</a>, which may differ across <a href="http://www.sciencedirect.com/science/article/pii/S1053811903007584" target="_blank">brain regions,</a> <a href="http://www.nature.com/nrn/journal/v5/n5/full/nrn1387.html" target="_blank">clinical populations</a>, or with respect to <a href="http://www.sciencedirect.com/science/article/pii/S0730725X07002172" target="_blank">drug effects</a>, thereby complicating interpretations of observed effects. Unlike the electrical potentials measured in EEG, however, the magnetic fields measured in MEG pass from the brain through the skull in a relatively undisturbed manner, substantially simplifying the inverse problem. In these ways, for a non-invasive technique, MEG best combines high temporal resolution and improves source localisation within the human brain.<br />
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What exactly do those tiny magnetic fields reflect about brain activity? When a neuron receives communication from a neighbour, an excitatory or inhibitory postsynaptic potential (<a href="http://en.wikipedia.org/wiki/Excitatory_postsynaptic_potential" target="_blank">EPSP</a> or <a href="http://en.wikipedia.org/wiki/Inhibitory_postsynaptic_potential" target="_blank">IPSP</a>) is generated in the neuron's dendrites, causing that local dendritic membrane to become <br />
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<tr><td style="text-align: center;"><a href="http://www.nature.com/scitable/content/ne0000/ne0000/ne0000/ne0000/134762971/megfinal_2_1.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="http://www.nature.com/scitable/content/ne0000/ne0000/ne0000/ne0000/134762971/megfinal_2_1.jpg" title="Fig. 5. The source of recorded magnetic fields in MEG. Adapted from Hansen et al. (2010)" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: xx-small; text-align: start;">Fig. 5. The source of recorded magnetic <br />fields in MEG. Adapted from Hansen et al. (2010)</span></td></tr>
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transiently depolarised relative to the body of the neuron. This difference in potential generates a current flow both inside and outside the neuron, which creates a magnetic field. One such event, however, is still insufficient in generating a magnetic field large enough to be detected even by the mightiest of SQUIDs, so it is thought that the fields measured in MEG are the result of at least 50,000 neurons simultaneously experiencing EPSPs or IPSPs within a certain region. Unfortunately, current technology and analysis methods are limited to detecting magnetic fields generated along the cortex, the bit of the brain closest to the scalp. Fields generated in deeper cortical and subcortical areas rapidly dissipate as they travel much longer distances through the brain. To complicate things further, we have to remember that magnetic fields obey Ampère's <a href="http://en.wikipedia.org/wiki/Right-hand_rule" target="_blank"><em>right-hand rule</em></a> which states that if a current flows in the direction of the thumb in a "thumbs-up" gesture of the right hand, the generated magnetic field will flow perpendicularly to the thumb, in the direction of the fingers. This means that only neurons oriented tangentially along the skull surface generate magnetic fields which radiate outwards in the direction of the skull to be measured at the surface. Fortunately, mother nature has cut scientists some slack here, as the pervasive folding pattern (<a href="http://white.stanford.edu/teach/index.php/Brain_Gyrification_and_its_Significance" target="_blank">gyrification</a>) of the brain's cortex provides us with plenty of neurons arranged in the direction useful for MEG measurement. The cortex alone is enough to keep scientists busy, and findings from fMRI and direct electrophysiological recordings from non-human animals provide complementary information about the world underneath the cortex, and how it may all fit together.<br />
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At the end of a long and arduous MEG scanning session, one is left with about 300 individual time series, typically recorded at 1000 Hz, reflecting tiny changes in magnetic fields driven by neural activity presumably occuring in response to some cognitive task. Although the shielded room blocks out magnetic interference from other electrical devices (and all equipment inside the room works through <a href="http://en.wikipedia.org/wiki/Optical_fiber" target="_blank">optical fibres</a>), there is still massive interference from the subject's heart and any other muscle activity around the head. For this reason, participants are typically instructed to limit eye movements and blinking and any remaining artefactual noise in the data (i.e., anything not thought to be brain activity) is taken out at the analysis stage using techniques like <a href="http://en.wikipedia.org/wiki/Independent_component_analysis" target="_blank">independent component analysis</a>.<br />
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<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: xx-small; text-align: start;">Fig. 6. Raw MEG data (left), and event-related <br />fields in sensor space and source space (right). <br />Adapted from Schoenfeld et al. (2014).</span></td></tr>
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Analysis of MEG data can be done in <em>sensor space</em>, in which one simply looks at how the signals at individual sensors change during different parts of a cognitive task. This provides a rough estimate of the activation patterns along the cortex. The perk of MEG, however, is the ability to project data recorded in the 300 sensors to <em>source space</em>, and effectively estimate where in the brain these signals may originate. Although this is certainly more feasible in MEG than EEG, the inverse problem is actual a fundamental issue to both types of extracranial recordings (we don't have this problem when measuring directly from the brain during<a href="http://www.nature.com/scitable/blog/brain-metrics/peering_directly_into_the_human" mce_href="http://www.nature.com/scitable/blog/brain-metrics/peering_directly_into_the_human" nated_pk="http://www.nature.com/scitable/blog/brain-metrics/peering_directly_into_the_human"> intracranial recording</a>). One way to narrow down which possible activation regions in the brain could underlie the observed magnetic fields is to establish certain assumptions about <a href="http://en.wikipedia.org/wiki/Neural_coding" target="_blank">what we expect brain activity to look like in general</a>, and how that activity is translated into the signal measured at the scalp. Such assumptions are more reasonable in MEG than EEG due to the higher fidelity of magnetic fields as they pass from the brain to scalp.<br />
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<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: xx-small; text-align: start;">Fig. 7. Neural activation is smooth, forming <br />clusters of active neurons. Adapted from Wiki Commons.</span></td></tr>
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For example, neural activation in the brain is assumed to be <em>smooth. </em>Imagine all the active neurons in a brain at a single point in time as stars in the sky: smooth activation would mean that the stars would form little clusters, rather than appear completely randomly all over the sky. Indeed, this feature of brain activation is what allows us to detect any magnetic fields using MEG in the first place! Remember that only many neurons within a local region which happen to be simultaneously active generate fields strong enough to be detected at the scalp. <br />
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<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: xx-small; text-align: start;">Fig. 8. MRI structural image of the head and brain (left), <br />and sensor, head, and brain model (right). <br />Adapted from Wiki Commons and OHBA.</span></td></tr>
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Another assumption is that the fate of the travelling magnetic fields depends on the physical size, shape, and organisation of the brain and scalp. To this end, MEG data across all 300 sensors are registered to an MRI scan of each participant's head and a 3D mapping of their scalp (obtained by literally marking hundreds of points along each participant's scalp using a digital pen), which together provide a high spatial resolution description of the anatomy of the entire head, brain included. These assumptions, among others, are used to mathematically estimate where in the brain the measured magnetic fields may have originated at each point in time. <br />
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<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: xx-small; text-align: start;">Fig. 9. Alpha, beta, and gamma oscillations. <br />Adapted from Wiki Commons.</span></td></tr>
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There are two general approaches when analyzing MEG data. Analysis of <em>event-related fields</em> looks at how the timing or the size of the magnetic</div>
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fields changes with respect to an event of interest during a cognitive task (e.g., the appearance of an image). The idea is that although there is a lot of noise in the measurement, if one averages many trials together the noise will cancel out, while the effect of interest, which always occurs in relation to a precisely timed event in the cognitive task, will remain. This follows in the tradition of EEG analysis, in which these evoked responses are called <a href="http://en.wikipedia.org/wiki/Event-related_potential" target="_blank">event-related <em>potentials</em></a>. Alternatively, one can use <a href="http://en.wikipedia.org/wiki/Fourier_transform" target="_blank">Fourier transformations</a> to break the data down into frequency components, also known as waves, rhythms, or <a href="http://en.wikipedia.org/wiki/Neural_oscillation" target="_blank">oscillations</a>, and measure changes in their phase or amplitude in response to cognitive events. This follows in the tradition established by Berger himself, who discovered and named <em>alpha </em>and <em>beta</em> waves. Neural oscillations have recently received a lot of attention as they are suggested to be involved in <a href="http://www.scholarpedia.org/article/Binding_by_synchrony" target="_blank">synchronizing the activity of populations of neurons</a>, and have been associated with a number of cognitive functions such as <a href="http://www.sciencedirect.com/science/article/pii/S1364661315000285" target="_blank">attentional control</a> and <a href="http://www.sciencedirect.com/science/article/pii/S0959438814002360" target="_blank">movement preparation</a>, as in the case of alpha and beta oscillations, respectively.<br />
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Other resources:<br />
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For a slightly more in-depth description of MEG, see <a href="http://pn.bmj.com/content/early/2014/03/19/practneurol-2013-000768.full" target="_blank">here</a>.<strong> </strong><br />
For a more in-depth description of MEG acquisition, see this <a href="https://www.youtube.com/watch?v=CPj4jJACeIs" target="_blank">video</a>.<strong> </strong><br />
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And for the kids, see this excellent article at <a href="http://kids.frontiersin.org/article/10.3389/frym.2014.00010" mce_href="http://kids.frontiersin.org/article/10.3389/frym.2014.00010" nated_pk="http://kids.frontiersin.org/article/10.3389/frym.2014.00010">Frontiers for Young Minds</a><br />
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<strong>References</strong><br />
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Baillet, S., Mosher, J.C., & Leahy, R.M. (2001). <em>Electromagnetic brain mapping</em>. IEEE Signal Processing Magazine..<br />
Fernando H, Lopes da Silva. MEG: an introduction to methods. eds: Hansen, Kringelback & Salmelin. USA: OUP, 2010:1-23, figure 1.3 from p6.<br />
La Vaque, T. J. (1999). The History of EEG Hans Berger: Psychophysiologist. A Historical Vignette. <em>Journal of Neurotherapy</em>. <br />
Proudfoot, M., Woolrich, M.W., Nobre, A.C., & Turner, M. (2014). Magnetoencephalography. <em>Pract Neurol</em>, 0, 1-8. <br />
Schoenfeld, M.A., Hopf, J-M., Merkel, C. Heinz, H-J., & Hillyard, S.A. (2014). Object-based attention involves the sequential activation of feature-specific cortical modules. <em>Nature Neuroscience, </em>17(4). <br />
Vrba, J. (2002). Magnetencephalography: the art of finding a needle in a haystack. <em>Physica C</em>, 368, 1-9.<br />
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The data figures are from papers cited above. All other figures are from Wiki Commons.StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-67798237630154176172015-05-16T02:02:00.003-07:002015-05-16T03:44:50.646-07:00What does fMRI measure?<div class="separator" style="clear: both; text-align: center;">
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiNSGIa66zXpkDlEHlS-CUDKuPGLXLUKHFjGOFyj81TaLe-ddnRpq-7mIj9zFFCh48VghO2zXLe3u10N2DzkE2qRAThjR6W6rhw9PPNnFIEpdH8-5O9fOafCXqk8cTkKCyxcBwD034B4Lx1/s1600/Fig1.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><span style="font-family: inherit;"><img border="0" height="137" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiNSGIa66zXpkDlEHlS-CUDKuPGLXLUKHFjGOFyj81TaLe-ddnRpq-7mIj9zFFCh48VghO2zXLe3u10N2DzkE2qRAThjR6W6rhw9PPNnFIEpdH8-5O9fOafCXqk8cTkKCyxcBwD034B4Lx1/s200/Fig1.png" width="200" /></span></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-family: inherit;">Fig 1. From <a href="http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_00577">Kuo, Stokes, Murray & Nobre (2014)</a></span></td></tr>
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<span style="font-family: inherit;">When you say ‘brain activity’, many people first think of
activity maps generated by functional magnetic resonance imaging (</span><a href="http://www.scholarpedia.org/article/Functional_magnetic_resonance_imaging" style="font-family: inherit;">fMRI</a><span style="font-family: inherit;">; see figure
1). As a non-invasive braining imaging method, fMRI has become the go-to workhorse
of cognitive neuroscience. Since the first papers were published in the early 1990s, there
has been an explosion of studies using this technique to study brain function, from basic perception to mind-reading for </span><a href="http://www.nejm.org/doi/full/10.1056/NEJMoa0905370" style="font-family: inherit;">communicating with locked-inpatients</a><span style="font-family: inherit;"> or </span><a href="http://www.the-scientist.com/?articles.view/articleNo/25028/title/Watching-the-Brain-Lie/" style="font-family: inherit;">detecting lies </a><span style="font-family: inherit;">in criminal investigations. At its best, fMRI
provides unparalleled access to detailed patterns of activity in the healthy human
brain; at its worst, fMRI could reduce to an expensive generator of 3-</span>dimensional<span style="font-family: inherit;"> Rorschach images. To understand the </span>relative strengths and weaknesses of fMRI, it is <span style="font-family: inherit;">essential to understand exactly what fMRI measures. Without delving too deeply into
the nitty-gritty (see below for further reading), we will cover the basics that
are necessary for understanding the potential and limits of this ever popular and
powerful tool.</span></div>
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<span style="font-family: inherit;">“fMRI does not directly measure brain activity”</span></blockquote>
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<span style="font-family: inherit;">First and foremost, electricity is the language of the
brain. At any moment, there are millions of tiny electrical impulses (<a href="http://en.wikipedia.org/wiki/Action_potential">action potentials</a>) whizzing around your brain. At synaptic junctions, these impulses release
specific chemicals (i.e., <a href="http://en.wikipedia.org/wiki/Neurotransmitter">neurotransmitters), </a>which in turn modulate the electrical
activity in the next cell. This is the fundamental basis for <a href="http://thebrain.mcgill.ca/flash/i/i_01/i_01_cl/i_01_cl_fon/i_01_cl_fon.html">neural communication</a>.
Somehow, these processes underpin every thought/feeling/action you have ever
experienced. Our challenge is to understand how these electric events give rise
to these phenomena of mind. <o:p></o:p></span></div>
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<span style="font-family: inherit;">However, fMRI does not exactly measure electrical activity (compare <a href="http://www.ohba.ox.ac.uk/groups/electroencephalography-eeg">EEG</a>, <a href="http://www.ohba.ox.ac.uk/groups/magnetoencephalography-meg">MEG</a>, <a href="http://www.nature.com/scitable/blog/brain-metrics/peering_directly_into_the_human">intracranial
neurophysiology</a>); but rather it measures the indirect consequences of
neural activity (<a href="http://en.wikipedia.org/wiki/Haemodynamic_response">the
haemodynamic response</a>). The pathway from neural activity to the fMRI activity
map is schematised in figure 2 below: <o:p></o:p></span><br />
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<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: 12.8000001907349px;">Fig 2. From </span><a href="http://www.ym.edu.tw/~cflu/fMRI_BOLD.pdf" style="font-size: 12.8000001907349px;">Arthurs & Boniface (2002)</a></td></tr>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjNusDdC_Pv5hCNYSJVM-R_89lzq1XBvy87txhpc_Eba3kyl9g13LUXELsKGsJq0hl3HwR-EFmWSzTwUTjsWRHSpy7R6aSLQDdHbxED8SmM7BOKLCIhJn1yamppKegnT5-WKfADnTwss5Jf/s1600/Fig3.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="173" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjNusDdC_Pv5hCNYSJVM-R_89lzq1XBvy87txhpc_Eba3kyl9g13LUXELsKGsJq0hl3HwR-EFmWSzTwUTjsWRHSpy7R6aSLQDdHbxED8SmM7BOKLCIhJn1yamppKegnT5-WKfADnTwss5Jf/s320/Fig3.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig 3. From <a href="http://www.oxfordsparks.net/video/a-spin-around-the-brain">Oxford Sparks</a></td></tr>
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<span style="font-family: inherit;">To summarise, let's consider three key principles: 1) neural activity is systematically
associated with changes in the relative concentration of oxygen in local blood
supply (figure 3); 2) oxygenated blood has different magnetic susceptibility relative to deoxygenated
blood; 3) changes in the ratio of oxygenated/de-oxygenated blood (<a href="http://en.wikipedia.org/wiki/Haemodynamic_response">haemodynamicresponse function</a>; figure 4) can be inferred with fMRI by measuring the <a href="http://en.wikipedia.org/wiki/Blood-oxygen-level_dependent">blood-oxygen-leveldependent</a> (BOLD) response.<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_yNWO_G66HY4bewJKhzKkxELjpoFfjA0618YAl4RRRoUSQc63vh9q-wzWPd8QNI8UOXZg0CWk529fj1BwsPdlVc1fJCcL9IS4AWQsxpJfaKlKgna26UtosVZwZnMLivHRg8qITe43425i/s1600/Fig4.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_yNWO_G66HY4bewJKhzKkxELjpoFfjA0618YAl4RRRoUSQc63vh9q-wzWPd8QNI8UOXZg0CWk529fj1BwsPdlVc1fJCcL9IS4AWQsxpJfaKlKgna26UtosVZwZnMLivHRg8qITe43425i/s200/Fig4.png" width="193" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><a href="http://en.wikipedia.org/wiki/Haemodynamic_response#/media/File:Haemodynamic_response_function.svg">Fig 4. Haemodynamic response function</a></td></tr>
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<span style="font-family: inherit;">So fMRI only provides an indirect measure of brain activity.
This is not necessarily a bad thing. Your classic thermometer does not directly
measure ‘temperature’, but rather the volume of mercury in a glass tube. Because
these two parameters are tightly coupled, a well calibrated thermometer does a nice
job of tracking temperature. The problem arises when the coupling is incomplete,
noisy or just very complex. For example, the haemodynamic response is probably
most tightly coupled to synaptic events rather than action potentials (see </span><a href="http://www.nature.com/nature/journal/v453/n7197/full/nature06976.html" style="font-family: inherit;">here</a><span style="font-family: inherit;">). This
means certain types of activity will be effectively invisible to fMRI,
resulting in systematic biases (e.g., favouring input (and local processing) to
output neural activity). The extent to which coupling depends on unknown (or
unknowable) variability also limits the extent to which we can interpret the
BOLD signal. Basic neurophysiological research is therefore absolutely
essential for understanding exactly what we are measuring when we switch on the
big scanner. See </span><a href="http://www.nature.com/nature/journal/v453/n7197/full/nature06976.html" style="font-family: inherit;">here</a><span style="font-family: inherit;">
for an authoritative review by Logothetis, a great pioneer in neural basis of
fMRI.</span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;">“spatial resolution”</span></blockquote>
<div class="MsoNormal">
<span style="font-family: inherit;">Just like your digital camera, a brain scan can be defined
by units of spatial resolution. However, because the image is</span><span style="font-family: inherit;"> 3D, we call these
volumetric pixels, or </span><a href="http://en.wikipedia.org/wiki/Voxel" style="font-family: inherit;">voxels </a><span style="font-family: inherit;">for short. In a typical scan, each voxel might cover
3mm</span><sup style="font-family: inherit;">3</sup><span style="font-family: inherit;"> of tissue, a volume that would encompass </span><a href="https://cfn.upenn.edu/aguirre/wiki/public:neurons_in_a_voxel" style="font-family: inherit;">~ 630,000 neurons</a><span style="font-family: inherit;">
in cortex. However, the exact size of the voxel only defines the theoretically maximal
resolution. In practice, the effective resolution in fMRI also depends on the
spatial specificity of the hemodynamic response, as well as more practical considerations
such as the degree of head movement during scanning. These additional factors can add substantial spatial distortion or blurring. Despite these limits, there are few methods with superior spatial resolution. Intracranial recordings can measure activity with excellent spatial precision (even <a href="http://en.wikipedia.org/wiki/Single-unit_recording">isolating activity from single cells</a>), but this invasive procedure is limited to animal models or very specific clinical conditions that require this level of precision for diagnostic purposes (see <a href="http://www.nature.com/scitable/blog/brain-metrics/peering_directly_into_the_human">here</a>). M</span><span style="font-family: inherit;">oreover, microscopic resolution isn't everything. If we focus in too closely without seeing the bigger picture, there is always the danger of not seeing the forest for all the trees. fMRI provides a good compromise between </span>precision and <span style="font-family: inherit;">coverage. Ultimately, we need to bridge different levels of analysis to capitalise on insights that can only be gained with microscopic precision and macroscopic measures that can track larger-scale network dynamics. </span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;"> “snapshot is more like
a long exposure photograph”</span></blockquote>
<div class="separator" style="clear: both; text-align: center;">
</div>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjwMUShI4TeQlph7rDnuqUwRihWvrDU46W48g-0alMffIqFxjAxKpTGJEek495hhuT19ne-pFHOCrYaj0TjXhVPYoPcZ6uS4QTcYIq8Ua6TGsaPuzyLqo0NmSD-vS9SZNxk_-BrwBspB5Wj/s1600/fig5.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="213" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjwMUShI4TeQlph7rDnuqUwRihWvrDU46W48g-0alMffIqFxjAxKpTGJEek495hhuT19ne-pFHOCrYaj0TjXhVPYoPcZ6uS4QTcYIq8Ua6TGsaPuzyLqo0NmSD-vS9SZNxk_-BrwBspB5Wj/s320/fig5.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig 5. <span style="font-family: "Calibri","sans-serif"; font-size: 11.0pt; line-height: 115%; mso-ansi-language: EN-GB; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">Wiki
<a href="http://en.wikipedia.org/wiki/Long-exposure_photography#/media/File:Paranal_Starry_Night.jpg">Commons</a></span></td></tr>
</tbody></table>
<div class="MsoNormal">
<span style="font-family: inherit;">Every student in psychology or neuroscience should be able to tell you that fMRI has good spatial
resolution (as above), but poor temporal resolution. This is because the haemodynamic
response imposes a fundamental limit on the time-precision of the measurement. Firstly, the peak response is delayed by approximately 4-6 seconds. However, this doesn’t really matter for offline analysis, because
we can simply adjust our recording to correct for this lag. The real problem is
that the response is extended over time. Temporal smoothing makes it difficult
to pinpoint the precise moment of activity. Therefore, the image actually
reflects an average over many seconds. Think of this like a very long long-exposure
photograph (see figure 5), rather than a snapshot of brain activity. This makes it very
difficult to study highly dynamic mental processes – fast neural
processes are simply blurred. Methods that measure electrical activity more directly have inherently higher temporal resolution (</span><a href="http://www.ohba.ox.ac.uk/groups/electroencephalography-eeg">EEG</a>, <a href="http://www.ohba.ox.ac.uk/groups/magnetoencephalography-meg">MEG</a>, <a href="http://www.nature.com/scitable/blog/brain-metrics/peering_directly_into_the_human">intracranial neurophysiology</a>)<span style="font-family: inherit;">.</span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;">“too much data to make sense of”</span></blockquote>
<div class="MsoNormal">
<span style="font-family: inherit;">A standard fMRI experiment generates many thousands of
measures in one scan. This is a major advantage of fMRI (mass
simultaneous recording), but raises a number of statistical challenges. Data
mining can be extremely powerful, however the intrepid data explorer will inevitably
encounter spurious effects, or false positives (entertain yourself with some fun
false positives <a href="http://www.tylervigen.com/spurious-correlations">here</a>).
<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: inherit;">This is more of an embarrassment of riches, rather than a
limit. I don’t believe that you can ever have too much data, the important
thing is to know how to interpret it properly (see </span><a href="http://the-brain-box.blogspot.co.uk/2012/05/truth-before-beauty-making-sense-of.html" style="font-family: inherit;">here</a><span style="font-family: inherit;">). Moreover, the same problem applies to other data-rich measures of brain activity. The solution is not to limit our recordings, but to improve our analysis </span>approaches<span style="font-family: inherit;"> to the multivariate </span>problem<span style="font-family: inherit;"> that is the brain (e.g., see <a href="http://the-brain-box.blogspot.co.uk/2013/06/research-briefing-dynamic-population.html">here</a>). <o:p></o:p></span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;">“too many free parameters”</span></blockquote>
<div class="MsoNormal">
<span style="font-family: inherit;">There are many ways to analyse an fMRI dataset, so which do
you choose? Especially when many of the available options make sense and can be easily
justified, but different choices generate slightly different results. This </span>dilemma<span style="font-family: inherit;"> will be </span>familiar<span style="font-family: inherit;"> to anyone who has ever analysed fMRI. A recent </span><a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3468892/" style="font-family: inherit;">paper</a><span style="font-family: inherit;"> identified 6,912 slightly different paths
through the analysis pipeline, resulting in</span><span class="apple-converted-space" style="font-family: inherit;"><span style="background: white;"> </span></span><span style="background-attachment: initial; background-clip: initial; background-color: white; background-image: initial; background-origin: initial; background-position: initial; background-repeat: initial; background-size: initial; font-family: inherit;">34,560</span><span style="font-family: inherit;"> different sets of results.
By fully exploiting this wiggle room, it should be possible to generate almost any
kind of result you would like (see </span><a href="http://blogs.discovermagazine.com/neuroskeptic/2012/06/30/false-positive-neuroscience/#.VVYRn_lVikp" style="font-family: inherit;">here</a><span style="font-family: inherit;">
for further consideration). Although this flexibility is not strictly a limit
in fMRI (and certainly not unique to fMRI), it is definitely something to keep in mind when interpreting what you read in the fMRI literature. It is important to define
the analysis pipeline independently of your research question, rather than try
them all and choose the one that gives you the ‘best’ result. Otherwise there
is a danger that you will only see what you want to see (i.e., </span><a href="http://www.ncbi.nlm.nih.gov/pubmed/19396166" style="font-family: inherit;">circular analysis</a><span style="font-family: inherit;">).<o:p></o:p></span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;">“…correlation, not causation”</span></blockquote>
<div class="MsoNormal">
<span style="font-family: inherit;">It is often pointed out the fMRI can only provide correlational evidence. The same can be said for</span> any other measurement technique. <span style="font-family: inherit;">Simply because a certain
brain area lights up with a specific mental function, we cannot be sure that the observed activity actually caused the mental event (see <a href="http://the-brain-box.blogspot.co.uk/2012/05/tale-of-two-evils-bad-statistical.html">here</a>). Only an interference
approach can provide such causal evidence. For example, if we ‘knock-out’ a
specific area (e.g., natural
occurring brain damage, <a href="http://www.ohba.ox.ac.uk/groups/transcranial-magnetic-stimulation-tms">TMS</a>, <a href="http://en.wikipedia.org/wiki/Transcranial_direct-current_stimulation">tDCS</a>, <a href="http://en.wikipedia.org/wiki/Ablative_brain_surgery">animal ablation studies</a>, <a href="http://en.wikipedia.org/wiki/Optogenetics">optogenetics</a>), and observe a specific impairment
in behaviour, then we can infer that the targeted area normally plays a causal role. </span><span style="font-family: inherit;">Although this is strictly correct, this does not necessarily
imply the causal methods are better. Neural recordings can provide enormously rich
insights into how brain activity unfolds during normal behaviour. In contrast,
causal methods allow you to test how the system behaves without a specific area. Because
there is likely to be redundancy in the brain (multiple brain areas capable of performing
the same function), interference approaches are susceptible to missing
important contributions. Moreover, perturbing the neural system is likely to
have knock-on effects that are difficult to control for, thereby complicating
positive effects. These issues probably deserve a dedicated post in the future.
But the point for now is simply to note that one approach is not obviously
superior to the other. It depends on the nature of the question.</span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;">“…the spectre of reverse inference”</span></blockquote>
<div class="MsoNormal">
<span style="font-family: inherit;">A final point worth raising is the spectre of reverse inference
making. In an influential <a href="http://ac.els-cdn.com/S1364661305003360/1-s2.0-S1364661305003360-main.pdf?_tid=166467f4-fb1a-11e4-ab7a-00000aacb35e&acdnat=1431705199_f9fca3ddb3402e7fb8b22e12e13f41f7">review
paper</a>, Russ Poldrak outlines the problem: <o:p></o:p></span></div>
<blockquote class="tr_bq">
<span style="font-family: inherit;">The usual kind of inference that is drawn from neuroimaging data
is of the form ‘<i>if cognitive process X is
engaged, then brain area Z is active</i>’. Perusal of the discussion sections
of a few fMRI articles will quickly reveal, however, an epidemic of reasoning
taking the following form:</span> </blockquote>
<blockquote class="tr_bq">
<br />
<ol>
<li><span style="font-family: inherit;">In the present study, when task comparison A was presented,
brain area Z was active.</span> </li>
<li><span style="font-family: inherit;">In other studies, when cognitive process X was putatively
engaged, then brain area Z was active.</span> </li>
<li><span style="font-family: inherit;">Thus, the activity of area Z in the present study demonstrates
engagement of cognitive process X by task comparison A.</span> </li>
</ol>
</blockquote>
<blockquote class="tr_bq">
<span style="font-family: inherit;">This is a ‘reverse inference’, in that it reasons backwards from
the presence of brain activation to the engagement of a particular cognitive
function.</span></blockquote>
<div class="MsoNormal">
<span style="font-family: inherit;">Reverse inferences are not a valid from of deductive reasoning,
because there might be other cognitive functions that activate the brain area.
Nevertheless, the general form of reasoning can provide useful information,
especially when t</span>he function of the particular brain area is relatively specific and particularly well-understood. Using accumulated knowledge to interpret new findings is necessary for theory building. However, in the asbence of a strict one-to-one mapping between structure and function, <span style="font-family: inherit;">reverse inference is best approached from a </span><a href="http://ac.els-cdn.com/S1364661305003360/1-s2.0-S1364661305003360-main.pdf?_tid=166467f4-fb1a-11e4-ab7a-00000aacb35e&acdnat=1431705199_f9fca3ddb3402e7fb8b22e12e13f41f7" style="font-family: inherit;">Bayesian
perspective rather than a logical inference</a><span style="font-family: inherit;">. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: inherit;"><u><br /></u></span></div>
<div class="MsoNormal">
<span style="font-family: inherit;"><u>Summary</u>: fMRI is one of the most popular methods in cognitive
neuroscience, and certainly the most headline grabbing. fMRI provides unparalleled
access to the patterns of brain activity underlying human perception, memory
and action; but like any method, there are important limitations. To appreciate
these limits, it is important understand some of the basic principles of fMRI. We
also need to consider fMRI as part of a broader landscape of available techniques,
each with their unique strengths and weakness (figure 6). The question is not so much: is
fMRI useful? But rather: is fMRI the right tool for my particular question. <o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEisvX9h1o5yqDSBBd_eanSTegY0qK7JTv0QIdr7UVvh94Iz2Sj4E0a8axyNQeC4d-4OtBa_8R6B8Bn2dma13bArZHtJ3BnrXm76ELn8qmwuOb0EeHuYwzjDznC7oLNwEwBfIl53pJ8PC8mH/s1600/fig6.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="266" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEisvX9h1o5yqDSBBd_eanSTegY0qK7JTv0QIdr7UVvh94Iz2Sj4E0a8axyNQeC4d-4OtBa_8R6B8Bn2dma13bArZHtJ3BnrXm76ELn8qmwuOb0EeHuYwzjDznC7oLNwEwBfIl53pJ8PC8mH/s400/fig6.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption">Fig 6. <a href="http://www.nature.com/neuro/journal/v17/n11/full/nn.3839.html">from Sejnowski, Churchland and Movshon, 2014, Nature Neuroscience</a></td></tr>
</tbody></table>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: inherit;">Further reading:<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: inherit;"><a href="http://imaging.mrc-cbu.cam.ac.uk/imaging/CbuImaging">CBUImaging wiki</a><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: inherit;"><a href="http://www.fmrib.ox.ac.uk/fmrib-about/what-is-fmri">FMRIB</a><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: inherit;"><a href="http://www.oxfordsparks.net/video/a-spin-around-the-brain">Oxford Sparks</a> (see below for video demo)<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: inherit;"><a href="http://fmri.ucsd.edu/Research/whatisfmri.html">UCSD</a></span></div>
<div class="MsoNormal">
<a href="http://neurologism.com/2013/01/23/what-does-fmri-measure-anyway/">Neurologism</a></div>
<div class="MsoNormal">
<span style="font-family: inherit;"><a href="http://blogs.discovermagazine.com/neuroskeptic/2010/08/19/fmri-analysis-in-1000-words/#.VVYQtflViko">Neuroskeptic</a></span></div>
<div class="MsoNormal">
<span style="font-family: inherit;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: inherit;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: inherit;">Key references </span></div>
<div class="MsoNormal">
<br /></div>
<div class="EndNoteBibliography" style="margin-bottom: .0001pt; margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; text-indent: -36.0pt;">
<!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN EN.REFLIST <span style='mso-element:
field-separator'></span></span><![endif]--><span lang="EN-US">Arthurs, O. J.,
& Boniface, S. (2002). How well do we understand the neural origins of the
fMRI BOLD signal? <i>Trends Neurosci, 25</i>(1),
27-31. doi: Doi 10.1016/S0166-2236(00)01995-0<o:p></o:p></span></div>
<div class="EndNoteBibliography" style="margin-bottom: .0001pt; margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; text-indent: -36.0pt;">
<span lang="EN-US">Logothetis, N. K. (2008). What we can do and what we cannot do with
fMRI. <i>Nature, 453</i>(7197), 869-878.
doi: DOI 10.1038/nature06976<o:p></o:p></span></div>
<div class="EndNoteBibliography" style="margin-bottom: .0001pt; margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; text-indent: -36.0pt;">
<span lang="EN-US">Poldrack, R. A. (2006). Can cognitive processes be inferred from
neuroimaging data? <i>Trends Cogn Sci, 10</i>(2),
59-63. doi: DOI 10.1016/j.tics.2005.12.004<o:p></o:p></span></div>
<div class="MsoNormal">
<!--[if supportFields]><span style='font-size:11.0pt;line-height:115%;
font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin;mso-fareast-font-family:
Calibri;mso-fareast-theme-font:minor-latin;mso-hansi-theme-font:minor-latin;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-ansi-language:EN-GB;mso-fareast-language:EN-US;mso-bidi-language:AR-SA'><span
style='mso-element:field-end'></span></span><![endif]--></div>
<div class="EndNoteBibliography" style="margin-left: 36.0pt; text-indent: -36.0pt;">
<span lang="EN-US">Sejnowski, T. J., Churchland, P. S., & Movshon, J. A. (2014).
Putting big data to good use in neuroscience. <i style="mso-bidi-font-style: normal;">Nat Neurosci, 17</i>(11), 1440-1441. <o:p></o:p></span></div>
<div class="EndNoteBibliography" style="margin-left: 36.0pt; text-indent: -36.0pt;">
<span lang="EN-US"><br /></span></div>
<div class="MsoNormal">
Fun demonstration from Oxford Sparks:</div>
<div class="MsoNormal">
<iframe allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/0uSYLy9itgg" width="560"></iframe>
</div>
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<br /></div>
<div class="EndNoteBibliography" style="margin-left: 36.0pt; text-indent: -36.0pt;">
<span lang="EN-US"></span></div>
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<br /></div>
<div>
<o:p><br /></o:p></div>
StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-49314937671723797032015-04-29T14:02:00.000-07:002015-04-30T12:14:28.094-07:00Peering directly into the human brain<div class="MsoNormal">
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTxYFn-wUKt9HbRE58q5NLSaVjY21YWLKGKpYBsQLN7W7gsfbqmv4A6_w_1uev94yVT5V_Mp993jTIxx1z0vW8Gbv87bW50H0haIvJZAGogE5Yqz8kEgAXTKVNGh1RUES3iPioVp-E8gAN/s1600/Neuronehisto.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTxYFn-wUKt9HbRE58q5NLSaVjY21YWLKGKpYBsQLN7W7gsfbqmv4A6_w_1uev94yVT5V_Mp993jTIxx1z0vW8Gbv87bW50H0haIvJZAGogE5Yqz8kEgAXTKVNGh1RUES3iPioVp-E8gAN/s1600/Neuronehisto.jpg" height="200" width="167" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><a href="https://commons.wikimedia.org/wiki/Neuron#/media/File:Neuronehisto.jpg">Wiki Commons</a></td></tr>
</tbody></table>
With the rise of non-invasive brain imaging such as functional magnetic resonance imaging (<a href="http://en.wikipedia.org/wiki/Functional_magnetic_resonance_imaging">fMRI</a>), researchers have been granted unprecedented access to the inner workings of the brain. It is now relatively straightforward to put your experimental subjects in an fMRI machine and measure activity 'blobs' in the brain. This approach has undoubtedly revolutionised cognitive neuroscience, and looms very large in people's idea of contemporary brain science. But fMRI has it's limitations. As every student in the business should know, fMRI has poor temporal resolution. fMRI is like a very long-exposure photograph: the activity snapshot actually reflects an average over many seconds. Yet the mind operates at the millisecond scale. This is obviously a problem. Neural dynamics are simply blurred with fMRI. However, probably more important is the theoretical limit.<br />
<br />
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhNavqda01LdkNhyphenhyphenz_lnpnBaCzc86bpLLZ1bWCqdK6CP9k1n5cAHo6oGBLTqEzhCe3EmXPRMCtjybjV-4sqPXV0EqxhsMJT50U8YNWdsVMgBySz_5o8Bji_HBPVmCinFAOhBeJNsdQuJrxf/s1600/ECoG.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhNavqda01LdkNhyphenhyphenz_lnpnBaCzc86bpLLZ1bWCqdK6CP9k1n5cAHo6oGBLTqEzhCe3EmXPRMCtjybjV-4sqPXV0EqxhsMJT50U8YNWdsVMgBySz_5o8Bji_HBPVmCinFAOhBeJNsdQuJrxf/s1600/ECoG.png" height="200" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Wiki in <a href="https://commons.wikimedia.org/wiki/File:Intracranial_electrode_grid_for_electrocorticography.png?uselang=en-gb">ECoG</a></td></tr>
</tbody></table>
Electricity is the language of the brain, but fMRI only measures changes in blood flow that are coupled to these electrical signals. This coupling is complex, therefore fMRI can only provide a relatively indirect measure of neural activity. Electroencephalography (<a href="http://en.wikipedia.org/wiki/Electroencephalography">EEG</a>) is a classic method for measuring actual <i>electrical </i>activity. It has been around for more than 100 years, but again, as every student should know: EEG has poor spatial resolution. It is difficult to know exactly where the activity is coming from. Magnetoencephalography (<a href="http://en.wikipedia.org/wiki/Magnetoencephalography">MEG</a>) is a close cousin of EEG. Developed more recently, MEG is better at localising the source of brain activity. But the fundamental laws of physics mean that any measure of electromagnetic activity from outside the head will always be spatially ambiguous (<a href="http://en.wikipedia.org/wiki/Inverse_problem">the inverse problem</a>). The best solution is to record directly from the surface of the brain. Here we discuss the unique opportunities in that arise in the clinic to measure electrical activity directly from the human brain using electrocorticography (<a href="http://en.wikipedia.org/wiki/Electrocorticography">ECoG</a>).<br />
<br />
<a href="http://en.wikipedia.org/wiki/Epilepsy">Epilepsy </a>can be a seriously debilitating neurological
condition. Although the symptoms can often be managed with medication, some
patients continue to have major seizures despite a cocktail of anti-epileptic
drugs. So-called intractable epilepsy affects every aspect of life, and can
even be life-threatening. Sometimes the only option is <a href="http://en.wikipedia.org/wiki/Neurosurgery">neurosurgery</a>: careful removal of the specific brain area responsible for seizures can
dramatically improve quality of life.<o:p></o:p></div>
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<tr><td class="tr-caption" style="text-align: center;"><a href="http://en.wikipedia.org/wiki/Neurosurgery#/media/File:Parkinson_surgery.jpg">Neurosurgery</a></td></tr>
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Psychology students should be familiar with the case of <a href="http://en.wikipedia.org/wiki/Henry_Molaison">Henry Molaison</a> (aka HM). Probably the most famous neuropsychology patient in history,
HM suffered intractable epilepsy until the neurosurgeon William Scoville removed
two large areas of tissue in the medial temporal lobe, including left and right
<a href="http://en.wikipedia.org/wiki/Hippocampus">hippocampus</a>. This pioneering surgery successfully treated his epilepsy, but
this is not why the case became so famous in neuropsychology. Unfortunately, the
treatment also left HM profoundly amnesic. It turns out that removing both
sides of the medial temporal lobe effectively removes the brain circuitry for
forming new memories. This lesson in functional neuroanatomy is what made the
case of HM so important, but there was also a important lesson for neurosurgery – be
careful which parts of the brain you remove!</div>
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The best way to plan a neurosurgical resection of epileptic
tissue is to identify exactly where the seizure is comping from. The best way to map out the affected region is to record activity directly from the surface of the
brain. This typically
involves neurosurgical implantation of <a href="http://en.wikipedia.org/wiki/Electrocorticography">recording electrodes </a>directly in the
brain to be absolutely sure of the exact location of the seizure focus. Activity can then be monitored over a number of days, or even weeks, for
seizure related abnormalities. This invasive procedure allows neurosurgeons to
monitor activity in specific areas that could be the source of epileptic
seizures, but also provides a unique opportunity for neuroscientific research.</div>
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<tr><td class="tr-caption" style="text-align: center;">From Pasley et al., 2012 PLoS Biol. Listen to audio <a href="http://journals.plos.org/plosbiology/article/asset?unique&id=info:doi/10.1371/journal.pbio.1001251.s009">here</a></td></tr>
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During the clinical observation period, patients are
typically stuck on the hospital ward with electrodes implanted in their brain literally waiting for a seizure to happen
so that the epileptic brain activity can be ‘caught on camera’. This
observation period provides a unique opportunity to also explore healthy brain
function. If patients are interested, they can perform some simple experiments
using computer based tasks to determine how different parts of the brain
perform different functions. Previous studies from some of the <a href="http://en.wikipedia.org/wiki/Wilder_Penfield">great pioneers in neuroscience </a>mapped out the motor cortex by stimulating different brain areas during neurosurgery. Current experiments are
continuing in this tradition to explore less well charted brain areas involved
in high-level thought. For example, in a
recent <a href="http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001251">study</a>
from Berkeley, researchers used novel brain decoding algorithms to convert
brain activity associated with internal speech into actual words. This research
helps us understand the fundamental neural code for the internal dialogue that
underlies much of conscious thought, but could also help develop novel tools
for providing communication to those otherwise unable to general natural speech.<br />
<span style="background-color: white; font-family: RobotoRegular, arial, sans-serif; font-size: 13px; line-height: 19px;"></span><br />
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhvYlzn40si24NDkKX9OE2ZBdmfca9220MU_PZLjVgx9EkH14ksnLX9bP8AkyVWMLQ3vLWXo7wfWUCJTnoUhJgsqLk6QmKjGU-UWXqW-GmllQrzDEIqflLL2FDl-h01KjvBjy7d0rt7gmlM/s1600/Parvizi.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhvYlzn40si24NDkKX9OE2ZBdmfca9220MU_PZLjVgx9EkH14ksnLX9bP8AkyVWMLQ3vLWXo7wfWUCJTnoUhJgsqLk6QmKjGU-UWXqW-GmllQrzDEIqflLL2FDl-h01KjvBjy7d0rt7gmlM/s1600/Parvizi.jpg" height="154" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">From <a href="http://www.nature.com/ncomms/2013/131015/ncomms3528/full/ncomms3528.html">Dastjerdi et al </a>2013 Nature Communications (watch video below)</td></tr>
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In Stanford, researchers were recently able to identify a brain area that
codes for numbers and quantity estimation (read study <a href="http://www.nature.com/ncomms/2013/131015/ncomms3528/full/ncomms3528.html">here</a>).
Critically, they were even able to show that this area is involved in everyday
use for numerical cognition, rather than just under their specific experimental conditions. See video below.<br />
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQ7ftPJHtqIN32n4_fa2goPO-D49f6mCl0NevNTg1mRmLFSOd0iGt6eWkxu1oGlSCb9e6WnwREwnsj42kpL_tXDdkXaMraeGCXV2XZKANh9OaePAj0HROzkxaJga8iAHT-Ydkp7aYL-tJj/s1600/TheMind.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQ7ftPJHtqIN32n4_fa2goPO-D49f6mCl0NevNTg1mRmLFSOd0iGt6eWkxu1oGlSCb9e6WnwREwnsj42kpL_tXDdkXaMraeGCXV2XZKANh9OaePAj0HROzkxaJga8iAHT-Ydkp7aYL-tJj/s1600/TheMind.png" height="200" width="137" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><a href="http://en.wikipedia.org/wiki/Portal:Mind_and_brain#/media/File:413px-RobertFudd17Jh.png">Wiki Commons</a></td></tr>
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The great generosity of these patients vitally contributes
to the broader understanding of brain function. They have dedicated their
valuable time in otherwise adverse circumstances to help neuroscientists explore
the very frontiers of the brain. These patients are true pioneers.<br />
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Key References<br />
<br />
<div class="EndNoteBibliography" style="margin-bottom: .0001pt; margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; text-indent: -36.0pt;">
<!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN EN.REFLIST <span style='mso-element:
field-separator'></span></span><![endif]--><span lang="EN-US">Dastjerdi, M.,
Ozker, M., Foster, B. L., Rangarajan, V., & Parvizi, J. (2013). Numerical
processing in the human parietal cortex during experimental and natural
conditions. <i>Nat Commun, 4</i>, 2528.<o:p></o:p></span></div>
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<span lang="EN-US"><br /></span></div>
<div class="EndNoteBibliography" style="margin-left: 36.0pt; text-indent: -36.0pt;">
<span lang="EN-US">Pasley, B. N., David, S. V., Mesgarani, N., Flinker, A., Shamma, S.
A., Crone, N. E., Knight, R. T., & Chang, E. F. (2012). Reconstructing
speech from human auditory cortex. <i style="mso-bidi-font-style: normal;">PLoS
Biol, 10</i>, e1001251.<o:p></o:p></span></div>
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Video showing the use of a number processing brain area in everyday use:<br />
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StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-15314761892410436542015-04-24T07:38:00.000-07:002015-04-24T07:38:07.595-07:00Research Briefing: organising the contents of working memory<div class="MsoNormal">
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<tr><td class="tr-caption" style="text-align: center;">Figure 1. Nicholas Myers</td></tr>
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<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">Research Briefing, by Nicholas Myers</span><br />
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<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">Everyone has been in this
situation: you are stuck in an endless meeting, and a colleague drones on about
a topic of marginal relevance. You begin to zone out and focus on the art
hanging in your boss’s office, when suddenly you hear your name mentioned. On
high alert, you suddenly shift back to the meeting and scramble to retrieve
your colleague’s last sentences. Miraculously, you are able to retrieve a few
key words – they must have entered your memory a moment ago, but would have
been quickly forgotten if hearing your name had not cued them as potentially
vital bits of information.<o:p></o:p></span></div>
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<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">This phenomenon, while elusive
in everyday situations, has been studied experimentally for a number of years
now: cues indicating the relevance of a particular item in working memory have
a striking benefit to our ability to recall it, <i>even if the cue is presented after the item has already entered memory</i>. See our previous <a href="http://the-brain-box.blogspot.co.uk/2013/02/research-briefing-attention-restores.html">Research Briefing </a>on how retrospective cueing can restore information to the focus of attention in working memory.<o:p></o:p></span></div>
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<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">In a new article, published in
the <a href="http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_00727">Journal of Cognitive Neuroscience</a>,
we describe a recent experiment that set out to add to our expanding knowledge
of how the brain orchestrates these retrospective shifts of attention. We were
particularly interested in the potential role of neural synchronization of 10
Hz (or alpha-band) oscillations, because they are important in similar <i>prospective</i> shifts of attention.<o:p></o:p></span></div>
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<tr><td class="tr-caption" style="text-align: center;">Figure 2. Experimental Task Design. <span style="font-size: 12.8000001907349px;">[from </span><a href="http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_00727" style="font-size: 12.8000001907349px;">Myers et al, 2014</a><span style="font-size: 12.8000001907349px;">]</span></td></tr>
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<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">We wanted to examine the
similarity of alpha-band responses (and other neural signatures of the engagement
of attention) both to retrospective and prospective attention shifts. We needed
to come up with a new task that allowed for this comparison. On each trial in
our task, experiment volunteers first memorized two visual stimuli. Two seconds later, a
second set of two stimuli appeared, so that a total of four stimuli was kept in
mind. After a further delay, participants recalled one of the four items. <o:p></o:p></span></div>
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<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">In between the presentation of
the first and the second set of two items, we sometimes presented a cue: this
cue indicated which of the four items would likely be tested at the end of the
trial. Crucially, this cue could have either a <i>prospective</i> or a <i>retrospective</i>
function, depending on whether it pointed to location where an item had already
been presented (a <i>retrospective</i> cue,
or retrocue), or to a location where a stimulus was yet to appear (a <i>prospective</i> cue, or precue). This
allowed us to examine neural responses to attention-guiding cues that were
identical with respect to everything but their forwards- or backwards-looking
nature. <o:p></o:p></span>See Figure 2 for a task schematic.</div>
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<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: 12.8000001907349px;">Figure 3. Results: retro-cueing and pre-cueing <br />trigger different attention-related ERPs.<br />[from <a href="http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_00727">Myers et al, 2014</a>]</span></td></tr>
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<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">We found marked differences in
event-related potential (<a href="http://en.wikipedia.org/wiki/Event-related_potential">ERP</a>) profiles between the precue and retrocue
conditions. We found evidence that precues primarily generate an anticipatory
shift of attention toward the location of an upcoming item: potentials just
before the expected appearance of the second set of stimuli reflected the
location where volunteers were attending. These included the so-called early directing attention negativity (or 'EDAN') and </span>the late directing attention-related positivity (or 'LDAP'; <span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">see Figure 3, middle panel; and see <a href="http://brainb.psyc.bbk.ac.uk/PDF/att_chapter13_newformat.pdf">here </a>for a review of attention-related ERPs). Retrocues elicited a different
pattern of ERPs that was compatible with an early selection mechanism, but not
with stimulus anticipation </span>(i.e., no LDAP, see Figure 3, upper panel). The latter seems plausible, since the cued
information was already in memory, and upcoming stimuli were therefore not
deserving of attention. In contrast to the distinct ERP patterns, alpha band
(8-14 Hz) lateralization was indistinguishable between cue types (reflecting,
in both conditions, the location of the cued item; see Figure 4).</div>
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<tr><td class="tr-caption" style="text-align: center;">Figure 4. Results: retro-cueing and pre-cueing trigger similar patters <br />of de-synchronisation in low frequency activity (alpha band at ~10Hz).<br /><span style="font-size: 12.8000001907349px;">[from </span><a href="http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_00727" style="font-size: 12.8000001907349px;">Myers et al, 2014</a><span style="font-size: 12.8000001907349px;">]</span></td></tr>
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<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">What did we learn from this
study? Taken together with the ERP results, it seems that alpha-band
lateralization can have two distinct roles: after a precue it likely enables
anticipatory attention. After a retrocue, however, the alpha-band response may reflect
the controlled retrieval of a recently memorized piece of information that has
turned out to be more useful than expected, without influencing the brain’s
response to upcoming stimuli. <o:p></o:p></span></div>
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<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">It seems that our senses are
capable of storing a limited amount of information on the off chance that it
may suddenly become relevant. When this turns out to be the case, top-down
control allows us to pick out the relevant information from among all the items quietly rumbling around in sensory brain regions.</span></div>
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<div class="MsoNormal">
<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">Many interesting questions
remain that we were not able to address in this study. For example, how do cortical
areas responsible for top-down control activate in response to a retrocue, and
how do they shuttle cued information into a state that can guide behaviour? </span><span lang="DE" style="font-family: "Helvetica Neue"; font-size: 11.0pt; mso-ansi-language: DE; mso-bidi-font-size: 12.0pt;"><o:p></o:p></span><br />
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<span style="color: windowtext; font-family: "Helvetica Neue"; font-size: 11.0pt; mso-bidi-font-size: 12.0pt;">Key Reference: </span><br />
<span style="font-family: Cambria, serif; font-size: 12pt;"><br /></span>
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi4IzUzbX-PfC7np1_QVN9lkzG4m1ZZ39Od-zxCmbLs4LdadVZuhT6_4u5wM3JghBjvVUZgQrRg1yb44_jC5ZdcL7yvoNqTphcxq-tWpv6ZVio4UFhHae0oyOqPY37yrfOqRINdzjVzGTt4/s1600/Picture1.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi4IzUzbX-PfC7np1_QVN9lkzG4m1ZZ39Od-zxCmbLs4LdadVZuhT6_4u5wM3JghBjvVUZgQrRg1yb44_jC5ZdcL7yvoNqTphcxq-tWpv6ZVio4UFhHae0oyOqPY37yrfOqRINdzjVzGTt4/s1600/Picture1.jpg" height="200" width="200" /></a><span style="font-family: Cambria, serif; font-size: 12pt;">Myers, Walther, Wallis, Stokes
& Nobre (2014) Temporal Dynamics of Attention during Encoding versus
Maintenance of Working Memory: Complementary Views from Event-related
Potentials and Alpha-band Oscillations. <i>Journal
of Cognitive Neuroscience</i> (<a href="http://www.mitpressjournals.org/doi/pdf/10.1162/jocn_a_00727">Open Access)</a></span></div>
StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-81042719831088733912015-04-10T03:47:00.000-07:002015-04-11T01:09:06.603-07:00Research Briefing: Preferential encoding of behaviourally relevant predictions revealed by EEG<div>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: right;"><tbody>
<tr><td style="text-align: center;"><img src="http://i.dailymail.co.uk/i/pix/2011/07/13/article-2014280-04E8D16C0000044D-314_468x286.jpg" height="195" style="margin-left: auto; margin-right: auto;" width="320" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Figure 1. Accurate predictions help us prepare the best action</td></tr>
</tbody></table>
</div>
Statistical regularities in the environment allow us to generate predictions to guide perception and action. For example, consider the challenge facing a goal keeper during a penalty shoot-out. There is simply not enough time to act responsively. By the time the ball in hurtling along its path to some deep corner of the net, it is probably already too late to plan and execute the appropriate action to save the goal. Instead, the goal keeper must actively predict the likely trajectory of ball before it has even left the boot of the other player. The goal keeper must use the any subtle clues betrayed by the kicker, any reliable signal to help prepare for a dive in the correct direction.<br />
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<h4>
Background</h4>
Predictions are useful in many contexts, not just professional sport. In everyday life, your brain is constantly generating predictions that help you to interpret the world around you and plan appropriate behaviour. <a href="http://en.wikipedia.org/wiki/Hermann_von_Helmholtz">Hermann von Helmholtz</a><span style="background-color: white; color: #222222; font-family: Arial, Tahoma, Helvetica, FreeSans, sans-serif; font-size: 13.1999998092651px; line-height: 18.4799995422363px;"> </span>described the importance of predictions derived from past experience for interpreting perceptual information (see previous <a href="http://the-brain-box.blogspot.co.uk/2012/05/research-briefing-how-memory-influences.html">post</a>). More recently, theorists that argued that the brain is essentially a predictive machine - for example, the <a href="http://en.wikipedia.org/wiki/Free_energy_principle">Free Energy Principle</a> proposes that perception and action are best conceptualised as a dynamic interplay between predictions we make about our environment and how well these predictions explain future events.<br />
<h4>
</h4>
<h4>
Research Question</h4>
In any given context, some predictions might be useful for behaviour, but others less so. Here, we asked whether and how the brain learns relevant and/or irrelevant predictive relationships using electroencephalography (EEG).<br />
<h4>
</h4>
<h4>
Methods Summary</h4>
<div style="text-align: justify;">
Participants in our experiment performed a simple target detection task (see Figure 2). On each experimental trial, they were presented with a visual stimulus drawn for a set of ten possible fractals images. At the start of the experiment, one image was assigned as the target. Participants were simply instructed to press a button as quickly as possible each time they detected the target image. Critically, unbeknownst to the participants. we also assigned specific roles for some of the other 'non-target' images. Firstly, we randomly assigned one of the stimuli to act as a task-relevant predictive cue. We rigged the presentation probabilities such that the target stimulus was more likely to follow the predictive cue than any other stimulus. We reasoned that participants should be able to implicitly learn this task-relevant predictive relationship to help prepare for the response to the target stimulus (faster response times). </div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg4_bKTUSHziEGVmi-cA7aVdgupirdwKX-yjteB-k3dOYFgqlgyEdoR1DOI7dF-Rfx8IqcnlZHz2kbDP-8_kybWICfxbLhhAmMACtzADSlq4nkneVe2i0wvjHkiuv70ec4ii1fseNjzNx-j/s1600/JT_task.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg4_bKTUSHziEGVmi-cA7aVdgupirdwKX-yjteB-k3dOYFgqlgyEdoR1DOI7dF-Rfx8IqcnlZHz2kbDP-8_kybWICfxbLhhAmMACtzADSlq4nkneVe2i0wvjHkiuv70ec4ii1fseNjzNx-j/s1600/JT_task.jpg" height="186" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: justify;">Figure 2. Behavioural task to compare neural processing associated with task-relevant and irrelevant statistical relationships. Presentation probabilities were randomly assigned for each participant at the start of the experiment [from <a href="http://journal.frontiersin.org/article/10.3389/fnhum.2014.00687/full">Stokes et al., 2014</a>]</td></tr>
</tbody></table>
Critically, we also included a task-irrelevant predictive relationship to test whether learning is specific to task-relevant relationships, or do participants implicitly encode all the regularities that they experience. Because this manipulation was by definition task-irrelevant, there was no behavioural index of learning. However, we could look to the EEG data to compare the neural response to task relevant versus irrelevant predictive relationships. <br />
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhxPsS-LqJ54BqGHDDMkI6zPNY-AkRGGfONeXiMsQV6TaSc3VuG2BEwSpcl9oEGneMsthujOCpkh9U03hbFCduCMlBa53NbE-0KgTVJmxgiXmXVrEKopzv9xnz7AutOK5VlEXq3aF3BDsiD/s1600/JT_RT.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhxPsS-LqJ54BqGHDDMkI6zPNY-AkRGGfONeXiMsQV6TaSc3VuG2BEwSpcl9oEGneMsthujOCpkh9U03hbFCduCMlBa53NbE-0KgTVJmxgiXmXVrEKopzv9xnz7AutOK5VlEXq3aF3BDsiD/s1600/JT_RT.jpg" height="200" width="196" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><div style="text-align: justify;">
<span style="font-size: 12.8000001907349px;">Figure 3. Reaction times became faster for cued targets </span></div>
<div style="text-align: justify;">
<span style="font-size: 12.8000001907349px;">as participants presumably learned the predictive </span></div>
<div style="text-align: justify;">
<span style="font-size: 12.8000001907349px;">nature of the task-relevant cue </span><span style="font-size: 12.8000001907349px;">[from </span><a href="http://journal.frontiersin.org/article/10.3389/fnhum.2014.00687/full" style="font-size: 12.8000001907349px;">Stokes et al., 2014</a><span style="font-size: 12.8000001907349px;">]</span></div>
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<h4>
</h4>
<h4>
Results Summary</h4>
<div>
Analysis of the reaction time data confirmed that participants learned the task-relevant statistical regularity that we introduced into the experiment. Reaction times were faster for cued targets relative to uncued targets (see Figure 3). By definition, there is no behavioural measure for task-irrelevant learning in this task, so we must turn to the EEG data (see Figure 4). Panel A shows the EEG response to cued targets relative to uncued targets as a function of learning (block number) in frontal, central and posterior scalp electrodes. The colour scale shows the difference in voltage between cued and uncued targets, i.e., the effect of the predictive cue on target processing. Towards the end of the experiment (blocks 7 & 8), a positive difference emerges at around 300ms after the presentation of the target. We also estimated the effect of learning by calculating the linear relationship between block number and the EEG response. In panel B, we can see the scalp distribution of this learning effect. In comparison to the robust learning effect of task-relevant predictions, we find no evidence for an effect of block (i.e., learning) on task-irrelevant predictions (Panel C & D). Finally, in Panel E we also plot the time-course of the learning effect for relevant (in blue) and irrelevant (in red) predictions for frontal, central and posterior electrodes, revealing a significant effect of learning relevant predictions (black significance bar, relative to baseline), but not irrelevant learning (relevant>irrelevant in grey significance bar). </div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGaDqFnv4SOi-DfILDOJaLjgwjwZ2vqBn6WAHclpN5z3WfZ3oWK51mkHNhXS1xvR4COFYjTAj6783VlW8huwQKSzG6d8gCXha0Batd3Qrj8LwjoInXYU8pascATkCA8aiJDac7z_XZUrSl/s1600/TargetEffects.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGaDqFnv4SOi-DfILDOJaLjgwjwZ2vqBn6WAHclpN5z3WfZ3oWK51mkHNhXS1xvR4COFYjTAj6783VlW8huwQKSzG6d8gCXha0Batd3Qrj8LwjoInXYU8pascATkCA8aiJDac7z_XZUrSl/s1600/TargetEffects.jpg" height="211" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: justify;">Figure 4. The EEG learning effect for cued vs. uncued targets and cued vs. uncued control non-targets. There was a robust effect of learning for predicted target stimuli (Panel A & B) relative to the task-irrelevant stimulus pairs (Panels C & D). In Panel E, we directly contrast the effect of learning task-relevant and irrelevant predictions in frontal, central and posterior electrodes, revealing a significant difference from around 250ms post-stimulus in central and posterior channels. Horizontal bars indicate significant regression slopes in the target learning condition compared to chance (in black; central: p = 0.053, cluster-corrected, dashed line, posterior: p = 0.0130, cluster-corrected, solid line), and directly compared to the control non-target condition (in grey; central: p = 0.026; posterior: p = 0.045, cluster corrected)<span style="font-size: 12.8000001907349px;"> </span><span style="font-size: 12.8000001907349px;">[from </span><a href="http://journal.frontiersin.org/article/10.3389/fnhum.2014.00687/full" style="font-size: 12.8000001907349px;">Stokes et al., 2014</a><span style="font-size: 12.8000001907349px;">]</span></td></tr>
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<div>
Finally, we performed the same analysis, but time-locked to the cue stimulus (Figure 5). All the conventions were the same, expect now we are looking at the response to the predictive stimulus (rather than the predicted stimulus). Again, we observed a robust learning effect for the task relevant predictive cue (Panels A,B), but not for the task-irrelevant (Panels A,B and C for the direct comparison). </div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj7-yby35ZIKdh6agYtQ-Dty29pnoHczqeMe9x659U-ljYgBB3ol05RCLRe0OcT3nT-CFPOxugmP8O6gQPNaazB-ZLQTUILnvFT66boDxuKFPEJjgototM1hMYagFdmmEzTfpPs1ZFR0VuY/s1600/CueEffects.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj7-yby35ZIKdh6agYtQ-Dty29pnoHczqeMe9x659U-ljYgBB3ol05RCLRe0OcT3nT-CFPOxugmP8O6gQPNaazB-ZLQTUILnvFT66boDxuKFPEJjgototM1hMYagFdmmEzTfpPs1ZFR0VuY/s1600/CueEffects.jpg" height="207" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: justify;">Figure 5. Event-related potentials to predictive stimuli: target cue and control non-target cues. All the conventions are the same as Figure 4. Note that there is a significant learning effect of the task-relevant predictive cue (Panel E, in blue), but not the task-irrelevant cue (in red)<span style="font-size: 12.8000001907349px;"> </span><span style="font-size: 12.8000001907349px;">[from </span><a href="http://journal.frontiersin.org/article/10.3389/fnhum.2014.00687/full" style="font-size: 12.8000001907349px;">Stokes et al., 2014</a><span style="font-size: 12.8000001907349px;">]</span></td></tr>
</tbody></table>
<h4>
</h4>
<h4>
Summary</h4>
<div>
This experiment shows that learning predictive relationships critically depends on the task relevance. In our experiment, participants were not explicitly informed about any of the statistical relationships between stimuli, but simply learned them through experience. Task-relevant predictions clearly benefited behaviour in the task. As participants learned the implicit statistics of the task, they responded more quickly to cued, relative to uncued targets. The learning effect was also clearly evident in EEG activity, consistent with differential processing of predictive, and predicted, task-relevant stimuli. In contrast, there was no evidence for a corresponding neural effect for task-irrelevant predictions, providing strong evidence that the brain prioritises which relationships to learn. Of course, our null effect does not mean that task-irrelevant predictions are never learned or represented, but rather highlights to importance of task-relevance in modulating the learning process. </div>
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<div>
As a side note, this experiment also provides a nice example of how we can use EEG to probe cognitive variables without requiring a behavioural response. In many situations, we are interested in how the brain processes non-target information. This presents an obvious challenge for a behavioural experiment: how can we measure processing, without making the stimulus task-relevant? Here, we use EEG to measure the response to task-irrelevant input, thereby providing insights at both the neural and cognitive level (see relevant post <a href="http://the-brain-box.blogspot.co.uk/2013/06/neuroscience-can-reveal-mysteries-of.html">here</a>)</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj7CMbT-xcRygb7rEskDti2IdB9KDriUpjg1YK-r4X1mG1tNAlAmEMq3rkK8vzpWbj639q3ErYThRac9RAI1x5K8eBpyu3mAsVFIRbEUxjrUKd_ablG-PL_bOxs2o-agGVtTncPzqoXfh7T/s1600/Picture1.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj7CMbT-xcRygb7rEskDti2IdB9KDriUpjg1YK-r4X1mG1tNAlAmEMq3rkK8vzpWbj639q3ErYThRac9RAI1x5K8eBpyu3mAsVFIRbEUxjrUKd_ablG-PL_bOxs2o-agGVtTncPzqoXfh7T/s1600/Picture1.jpg" height="200" width="200" /></a>Key reference: </div>
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<span style="font-family: "Cambria","serif"; font-size: 12.0pt; mso-ansi-language: EN-GB; mso-bidi-font-family: Arial; mso-bidi-language: AR-SA; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-US;">Stokes</span><span style="font-family: "Cambria","serif"; font-size: 12.0pt; mso-ansi-language: EN-GB; mso-bidi-font-family: Arial; mso-bidi-language: AR-SA; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-US;">, Myers, Turnbull &
Nobre (2014). Preferential encoding of behaviourally relevant predictions revealed
by EEG. <i>Frontiers in Human Neuroscience, </i>8:687 [<a href="http://journal.frontiersin.org/article/10.3389/fnhum.2014.00687/full">open access</a>]</span></div>
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StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-37761313773507799812015-04-09T10:14:00.001-07:002015-04-09T10:14:28.820-07:00New arrival, keeping us all busyIt has been a while since I have posted anything new, but in the meantime this little guy has arrived in our lives:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEglVkh7jlfCW10hK2Q-Ks3uWtpxkh_QO_YuOx8Ujhhdysv09sjWU5TYQSqlWTVGgzFKfc6WHUx98tdfAaHlmHF8HFvsAQkXF4IeDc9R-zG4XeysnECHYRC1K2vZDnivbxJ5QmYEa8ZlHqOX/s1600/IMG_0252.JPG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEglVkh7jlfCW10hK2Q-Ks3uWtpxkh_QO_YuOx8Ujhhdysv09sjWU5TYQSqlWTVGgzFKfc6WHUx98tdfAaHlmHF8HFvsAQkXF4IeDc9R-zG4XeysnECHYRC1K2vZDnivbxJ5QmYEa8ZlHqOX/s1600/IMG_0252.JPG" height="239" width="320" /></a></div>
<br />StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-46537607438282857782014-06-12T04:38:00.000-07:002014-06-12T04:38:40.924-07:00Research Briefing: Oscillatory Brain State and Variability in Working Memory<div class="MsoNormal">
<b><span style="font-family: "Arial","sans-serif";">Hot off the press: Oscillatory Brain
State and Variability in Working Memory</span></b><o:p></o:p></div>
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<br /></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">In a new paper, Nick Myers and
colleagues show how spontaneous fluctuations in </span><o:p></o:p></div>
<div class="MsoNormal">
<span style="font-family: "Arial","sans-serif";">alpha-band synchronization over visual
cortex predict the trial-by-trial accuracy of items stored in visual working
memory. The pre-stimulus desynchronization of alpha oscillations correlated
with the accuracy of memory recall. A model-based analysis indicated that this
effect arises from a modulation in the precision of memorized items, but not
the likelihood of remembering them (the recall rate). The <i>phase</i> of
posterior alpha oscillations preceding the memorized item also predicted memory
accuracy. The study highlights the influence of spontaneous changes in cortical
excitability on higher visual cognition, and how these state changes contribute
to large amounts of variability in what is normally thought of as a stable
aspect of behavior.</span><o:p></o:p></div>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiEw7qwn0_0q9P3Sgc71UPYjeNo8TpjBEyLmYN1HHsjcoA-UZxYJXHFpdOtprfCpovT1l-yH8kJGfit6lpFd1YN3BhIYBfPUTfYIBp6g5z3eUtXMBy71bvRcPW2FkRiqMRVw5gYNRC0mbKT/s1600/Myers2014+fig2.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiEw7qwn0_0q9P3Sgc71UPYjeNo8TpjBEyLmYN1HHsjcoA-UZxYJXHFpdOtprfCpovT1l-yH8kJGfit6lpFd1YN3BhIYBfPUTfYIBp6g5z3eUtXMBy71bvRcPW2FkRiqMRVw5gYNRC0mbKT/s1600/Myers2014+fig2.png" height="256" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">From Figure 2 in Myers et al. (2014)</td></tr>
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<span style="font-family: "Arial","sans-serif";">Reference: </span></div>
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Myers, N. E., M. G. Stokes, et al. (2014). "Oscillatory brain state predicts variability in working memory." J Neurosci 34(23): 7735-7743 <a href="http://www.jneurosci.org/content/34/23/7735.short" style="font-family: Arial, sans-serif;">http://www.jneurosci.org/content/34/23/7735.short</a><div class="MsoNormal">
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StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-3770170535462516072013-08-13T06:34:00.000-07:002013-08-13T12:16:50.602-07:00In the News: Death wave<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjc5zde0yai1Z8-IgbGXbj2R7xrl-DtxfRRz5D18GlgdHVaDtwOx4bZkOq5F9L0HS3C1PS54eZoZNSA2J4PFtdC8UoL_QxZNuOazQ-k0IHiC3PE3NKn9zf-u7B0mlGVnrfrUdrtzJCgr2-e/s1600/Near-Death-Experience_Illustration2.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjc5zde0yai1Z8-IgbGXbj2R7xrl-DtxfRRz5D18GlgdHVaDtwOx4bZkOq5F9L0HS3C1PS54eZoZNSA2J4PFtdC8UoL_QxZNuOazQ-k0IHiC3PE3NKn9zf-u7B0mlGVnrfrUdrtzJCgr2-e/s320/Near-Death-Experience_Illustration2.jpg" width="140" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Near-death Experience<br />
(Wiki Commons)</td></tr>
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Can neuroscience shed light on one of life's biggest mysteries - death? In a paper just published in <a href="http://www.pnas.org/content/early/2013/08/08/1308285110.full.pdf">PNAS</a>, researchers describe a surge of brain activity just moments before death. This raises the fascinating possibility that they have identified the neural basis for <a href="http://en.wikipedia.org/wiki/Near-death_experience">near death experiences</a>.<br />
<br />
First, to put this research into context, death-related brain activity was examined in rats, not humans. For obvious reasons, it is easier to study the death process in animals rather than humans. In this study, nine rats were implanted with electrodes in various brain regions, anaesthetised then '<a href="http://en.wikipedia.org/wiki/Animal_euthanasia">euthanized</a>' (i.e., killed). The exact moment of death was identified as the last regular heartbeat (<a href="http://en.wikipedia.org/wiki/Clinical_death">clinical death</a>). Electroencephalogram (<a href="http://en.wikipedia.org/wiki/Electroencephalography">EEG</a>) was recorded during normal waking phase, anaesthesia and after cardiac arrest (i.e., after death) from right and left frontal (RF/LF), parietal (RP/LP) and occipital (RO/LO) cortex (see <b>Figure</b> below). Data shown in panel A ranges from about 1hr before death to 30mins afterwards. At this coarse scale you can see some patterns in the waking data that generally reflect high frequency brain activity (gamma band, >40Hz). During anaesthesia, activity becomes synchronised at lower frequency bands (especially delta band: 0.1–5 Hz), but everything seems to flatline after cardiac arrest. However, if we now zoom in on the moment just after death (Panels B and C), we can see that the death process actually involves a sequence of structured stages, including a surge of high-frequency brain activity that is normally associated with wakefulness.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiUhJoY7KhJ_ROergwPj0s1_JqUwgQP6dwb32LckQCWP_x7fy3FJD1KyA7WGVi-q3Cl4_sgnMr4nvxukcDx_ncprBt2Am-Jq63YTWPMCv_oQRlBZTbVmXqpnj5h52_MAAw_bCcuGDEl6Ym7/s1600/Deathwave.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="224" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiUhJoY7KhJ_ROergwPj0s1_JqUwgQP6dwb32LckQCWP_x7fy3FJD1KyA7WGVi-q3Cl4_sgnMr4nvxukcDx_ncprBt2Am-Jq63YTWPMCv_oQRlBZTbVmXqpnj5h52_MAAw_bCcuGDEl6Ym7/s640/Deathwave.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Adapted from Fig 1 of Borjogin et al. (2013)</td></tr>
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In the figure above, Panel B shows brain activity zoomed in at 30min after death, and Panel C provides an even closer view, with activity from each brain area overlaid in a different colour. The authors distinguish four distinct cardiac arrest stages (CAS). CAS1 reflects the time between the last regular heartbeat and the loss of oxygenated blood pulse (mean duration ~4 seconds). The next stage, CAS2 (~6 seconds duration) ended with a burst in delta waves (so-called 'delta blip' ~1.7 seconds duration), and CAS3 (~20 seconds duration) continued until there was no more evidence of meaningful brain activity (i.e., CAS4 >30mins duration). These stages reflect an organized series of brain states. First, activity during CAS1 transitions from the anaesthetised state with an increase in high-frequency activity (~130Hz) across all brain areas. Next, activity settles into a period of low-frequency brain waves during CAS2. Perhaps most surprisingly, during CAS3 recordings were dominated by mid-range gamma activity (brain waves ~35-50Hz). In further analyses, they also demonstrate that this post-mortem brain activity is also highly coordinated across brain areas and different frequency bands. These are the hallmarks of high-level cognitive activity. In sum, these data suggests that long after death, the brain enters a brief state of heightened activity that is normally associated with wakeful consciousness.<br />
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<h4>
Heightened awareness just after death </h4>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHF5tVCkm8edoUfUrvB4CovOIzv9s3gzUg0A9Qlyqs0bh5SrV0EqoGWZooc2GtFLR_nDESiJhv3YVwWvLAKGe9Kg-8T8tRM4M-AfODq7W5nkGi7yj9wRSpr8X9MIHb1jK_S0QxTCkH8SSp/s1600/Deathwave2.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHF5tVCkm8edoUfUrvB4CovOIzv9s3gzUg0A9Qlyqs0bh5SrV0EqoGWZooc2GtFLR_nDESiJhv3YVwWvLAKGe9Kg-8T8tRM4M-AfODq7W5nkGi7yj9wRSpr8X9MIHb1jK_S0QxTCkH8SSp/s320/Deathwave2.png" width="205" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Adapted from Fig 2 of Borjogin et al. (2013)</td></tr>
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The authors even suggest that the level of activity observed during CAS3 may not only resemble the waking state, but might even reflect a <b>heightened state of conscious awareness</b> similar to the “highly lucid and realer-than-real mental experiences reported by near-death survivors”. This is based on the observation that there is more evidence for consciousness-related activity during this final phase of death than during normal wakeful consciousness. This claim, however, depends critically on their quantification of 'consciousness'. To date, there is no simple index of 'consciousness' that can be reliability measured to infer the true state of awareness. And even if we could derive such a <b>consciousness metric</b> in humans (see <a href="http://en.wikipedia.org/wiki/Integrated_information_theory">here</a>), to generalise to animals could only ever be speculative. Indeed, research in animals can only ever hint at human experience, including near-death experiences.<br />
<br />
Nevertheless, as the authors note, this research certainly demonstrates that activity in the brain is consistent with active cognitive processing. The results demonstrate that a neural explanation for these experiences is at least plausible. They have identified the right kind of brain activity for a neural explanation of near-death experiences, yet it remains to be verified whether these signatures do actually relate directly to the subjective experience.<br />
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<i>Future directions</i>: The obvious next step is to test weather similar patterns of brain activity are observed in humans after <a href="http://en.wikipedia.org/wiki/Clinical_death">clinical death</a>. Next, it will be important to show that such activity is strongly coupled to near-death experience. For example, does the presence or absence of such activity predict whether or not the person would report a near death experience. This second step is obviously fraught with technical and ethical challenges (think: The Flatliners), but would provide good evidence to link the neural phenomena to the phenomenal experience.<br />
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Key Reference:<br />
<br />
Borjigin, Lee, Liu, Pal, Huff, Klarr, Sloboda, Hernandez, Wang & Mashour (2013) Surge of neurophysiological coherence and connectivity in the dying brain. PNAS<br />
<br />
Related references:<br />
<br />
Tononi G (2012) Integrated information theory of consciousness: An updated account. Arch Ital Biol 150(2-3):56–90.<br />
<br />
Auyong DB, et al. (2010) Processed electroencephalogram during donation after<br />
cardiac death. Anesth Analg 110(5):1428–1432<br />
<br />
Related blogs and news articles:<br />
<br />
<a href="http://www.bbc.co.uk/news/science-environment-23672150">BBC News</a><br />
<a href="http://www.theguardian.com/science/head-quarters/2013/aug/12/dying-brains-conscious-experience">Headquarters</a> Hosted by the Guardian<br />
<a href="http://phenomena.nationalgeographic.com/2013/08/12/in-dying-brains-signs-of-heightened-consciousness/">National Geographic</a><br />
<a href="http://www.independent.co.uk/news/science/light-at-the-end-of-the-tunnel-for-scientists-studying-neardeath-experiences-8758148.html">The Independent</a><br />
<br />StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com2tag:blogger.com,1999:blog-8524853819129983649.post-3146950319059879182013-06-27T09:26:00.000-07:002013-06-27T09:26:27.079-07:00Book Review: Hallucinations, by Oliver Sacks<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhrf0PACHEISKefR7XwNk0wKPuVjR8DSUYjXppXuf7GH44sO-3voVkcsKO-oDb-CUe87RUwNMaxR1lAG-WifDihJDtDBEmR_V9rnVFBv8qGHF2nsLH31-qwBcPCg4sNWxkUoZd427lwCA2u/s127/George+Wallis.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhrf0PACHEISKefR7XwNk0wKPuVjR8DSUYjXppXuf7GH44sO-3voVkcsKO-oDb-CUe87RUwNMaxR1lAG-WifDihJDtDBEmR_V9rnVFBv8qGHF2nsLH31-qwBcPCg4sNWxkUoZd427lwCA2u/s127/George+Wallis.jpg" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">George Wallis</td></tr>
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<i><span lang="EN-US">This
is a guest post by George Wallis, one of my PhD students. We recently attended a seminar in which
Oliver Sacks discussed his recent book ‘Hallucinations’. In this post George discusses the ways in which
hallucinations provide neuroscientists with clues about the hidden workings of
the brain. This article is also cross-posted at <a href="http://www.nature.com/scitable/blog/brain-metrics/what_do_hallucinations_tell_us">Brain Metrics</a>, a </span></i><i><span lang="EN-US">Scitable Blog </span></i><i><span lang="EN-US">hosted by Nature Education. </span></i></div>
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<span lang="EN-US"><a href="http://en.wikipedia.org/wiki/Oliver_Sacks">Oliver Sacks</a> is a
neurologist and a writer, and close to a household name. For many readers, he will be a familiar
figure. Since 1970 he has been writing
humane accounts of the ways in which different forms of neurological illness or
damage affect the lives of his patients – or occasionally Sacks himself. Amongst his book-length works are <i><a href="http://en.wikipedia.org/wiki/The_Man_Who_Mistook_His_Wife_for_a_Hat">The
Man Who Mistook His Wife For a Hat</a>, </i>and <i><a href="http://en.wikipedia.org/wiki/Awakenings_(book)">Awakenings</a>,
</i>an account of the almost miraculous effect of the drug <a href="http://en.wikipedia.org/wiki/L-DOPA">l-DOPA</a> on sleeping sickness
patients at the Beth Abraham hospital, that has been adapted into a feature
film starring Robin Williams. Mark and I
were lucky to be invited to a small discussion session with Dr Sacks at Warwick
University where he is a visiting professor.
The topic of discussion was his most recent book, <i>Hallucinations.</i><o:p></o:p></span></div>
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<i><span lang="EN-US">Hallucinations
</span></i><span lang="EN-US">is known for its detailed account of Sack’s own
hallucinatory experiences during his remarkably excessive drug-taking phase in
the 1960s. Before tight drug laws, and
with access to the most potent compounds to be found in a doctor’s medicine
cabinet, Sacks experimented with a wide range of compounds – often in huge
doses. He describes the mind-altering
experiences he had with classic psychedelics, the disturbingly real-seeming
hallucinations experienced whilst on Artane, frightening episodes of psychotic
delirium following withdrawal from some lesser known toxic agents, and the
time-eating stupor of opiates. Most
fascinating for Sacks fans is his description of the amphetamine fuelled
epiphany that crystallized his desire to write about the neurology and the
experiences of his patients.<o:p></o:p></span></div>
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<span lang="EN-US">Beyond the spectacle of these
autobiographical chapters, Sacks’ book is a catalogue of the many varieties of
hallucination. For students of
neuroscience, this makes for engrossing reading. Hallucinations can tell us a lot about the
brain.<o:p></o:p></span></div>
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<span lang="EN-US">What are hallucinations? Sack’s defines them as ‘percepts arising in
the absence of any external reality – seeing things or hearing things that are
not there’. A few hundred years ago
hallucinations might have been ascribed to the influence of Gods or ghosts. Nowadays, neuroscientists and psychologists
see hallucinations as the result of abnormal activity in the brain. Crucially, neuroscientists consider <i>all </i>of the things we experience to
result from models the brain builds.
When you look at something in the outside world, your brain doesn’t
magically ‘reach out and touch’ the object so you can perceive it (though, some
philosophers might disagree with neuroscientists on this point!). Instead, the brain builds a model of what is
probably out there in the world, doing its best to match the model to the
sensory input we receive at our sense organs (for example, in the retina of the
eye). The things you perceive reflect
the model the brain builds – a model built out of the buzzing activity of
billions of neurons in your brain. It’s
basically intelligent guesswork, but mostly our brains do pretty well, and we
have the impression of a stable world.
Importantly, we tend to agree with other people about what’s out there -
which gives an indication that our brains are getting things right! However, if the activity of the brain is in
some way altered by a neurological disturbance of one form or another (illness,
drugs, damage from a stroke or injury), the model can diverge from its normal
faithful representation of the outside world, and we can have hallucinatory
perceptions.<o:p></o:p></span></div>
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<span lang="EN-US">Depending on the type of neural
disturbance, these hallucinations can take many different forms. These are all interesting to neuroscientists,
as they all have the potential to tell us something about the workings of the
brain.<o:p></o:p></span></div>
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<span lang="EN-US">For example, there is <a href="http://en.wikipedia.org/wiki/Charles_Bonnet_syndrome">Charles Bonnet
Syndrome,</a> which Sacks describes in his opening chapter. The brain’s intelligent guesswork about the
outside world is normally informed by a stream of activity from the sense
organs. What happens if you cut off that stream of incoming information? In some cases, the brain keeps on ‘making up
a story’ – except now, it has no information to go on, so the percepts that are
produced bear no relation to reality.
For example, diseases of the eye can deprive someone of the visual input
their brain has been used to receiving.
If part of the retina is damaged, this can leave a blind patch called a <a href="http://en.wikipedia.org/wiki/Scotoma">‘scotoma’</a>, and people with a
scotoma can sometimes have vivid hallucinations in just their blind patch. <o:p></o:p></span></div>
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<span lang="EN-US">Charles Bonnet type hallucinations can also
occur if someone goes completely blind. These
hallucinations can be highly ornate – for example little ‘lilliputian’ people
are sometimes seen, often in very colorful and ornate clothing. Some people describe these hallucinations as
being like a movie. For most people,
however, Charles Bonnet syndrome involves simpler hallucinations – shapes,
colours and patterns. The patterns in
the scotoma can ‘scintillate’, giving the impression of constant movement.<o:p></o:p></span></div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhYk3JI3WnL8M0X-9kW7IXG2Ir1IhsIt1MDF-R8AYAO2WKaI5yB3KIHtpq429TQrV0fHCA4eAKEXozvE0NoiDfIDFmEGGzu_KOlsQ8VcOmHvvBGDeBDljM2wMaD-MgbkGTUGf4-yrN-egE4/s344/scotoma.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="241" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhYk3JI3WnL8M0X-9kW7IXG2Ir1IhsIt1MDF-R8AYAO2WKaI5yB3KIHtpq429TQrV0fHCA4eAKEXozvE0NoiDfIDFmEGGzu_KOlsQ8VcOmHvvBGDeBDljM2wMaD-MgbkGTUGf4-yrN-egE4/s320/scotoma.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small; text-align: start;">Scintillating scotoma patterns</span></td></tr>
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<span lang="EN-US">Just because the retina is damaged doesn’t
imply that the visual parts of the brain are damaged too – this isn’t necessary
for hallucination. Charles Bonnet
syndrome reflects the normal activity of a brain forced to guess in the absence
of information – and people with Charles Bonnet are often well aware that their
hallucinations aren’t real, even if they seem very solid and detailed. Interestingly, some people with disrupted
sensory input experience hallucinations and some do not – it isn’t clear why.<o:p></o:p></span></div>
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<span lang="EN-US">Does this mean that you could hallucinate
too if you were deprived of sensory input?
Yes – though as with Charles Bonnet syndrome, it seems to vary from
person to person. There have been
various experiments with sensory deprivation.
A recent <a href="http://www.ncbi.nlm.nih.gov/pubmed/15179062">example</a>
was published in the <i>Journal of
Neuro-opthalmolagy </i>in 2004, by Lofti Merabet, Alvaro Pascual-Leone, and
their collaborators </span><!--[if supportFields]><span lang=EN-US><span
style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN PAPERS2_CITATIONS
<citation><uuid>6AB1C2B8-75C5-4C4A-8A9E-B563576D7034</uuid><priority>0</priority><publications><publication><volume>24</volume><publication_date>99200406011200000000222000</publication_date><number>2</number><startpage>109</startpage><title>Visual
Hallucinations During Prolonged Blindfolding in Sighted
Subjects</title><uuid>261CDDCF-91C4-4003-814D-ADF0BBC8653A</uuid><subtype>400</subtype><type>400</type><url>http://journals.lww.com/jneuro-ophthalmology/Fulltext/2004/06000/Visual_Hallucinations_During_Prolonged.3.aspx</url><bundle><publication><title>Journal
of Neuro-Ophthalmology</title><type>-100</type><subtype>-100</subtype><uuid>61888986-7BB8-4C8F-B5DB-4D8BA0C3F82B</uuid></publication></bundle><authors><author><firstName>Lotfi</firstName><middleNames>B</middleNames><lastName>Merabet</lastName></author><author><firstName>Denise</firstName><lastName>Maguire</lastName></author><author><firstName>Aisling</firstName><lastName>Warde</lastName></author><author><firstName>Karin</firstName><lastName>Alterescu</lastName></author><author><firstName>Robert</firstName><lastName>Stickgold</lastName></author><author><firstName>Alvaro</firstName><lastName>Pascual-Leone</lastName></author></authors></publication></publications><cites></cites></citation><span
style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US">(Merabet et al., 2004)</span><!--[if supportFields]><span lang=EN-US><span
style='mso-element:field-end'></span></span><![endif]--><span lang="EN-US">. They simply blindfolded thirteen healthy
volunteers for four days – otherwise, their volunteers were able to walk inside
and outside, talk to others, and listen to the TV. 10 out of 13 people reported
hallucinations. Just like in Charles
Bonnet syndrome, these were sometimes simple (flashing lights, geometric
patterns) and sometimes complex (landscapes, people, buildings, sunsets – often
seeming extremely vivid; more vivid than normal visual perceptions).<o:p></o:p></span></div>
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<span lang="EN-US">Hallucinations resulting from sensory
deprivation are evidence for the neuroscientists’ view of perception – that the
brain generates a model and fits it to the world. Sometimes the brain tissue responsible for
generating that model is disturbed in a way that alters the things people
perceive. For example, in <a href="http://en.wikipedia.org/wiki/Epilepsy">epilepsy</a>, the normally
controlled activity of the brain briefly goes haywire. Out of control neuronal firing emerges, and can
spread over the brain surface. Another
form of disturbed brain activity is experienced by many people in the form of <a href="http://en.wikipedia.org/wiki/Migraine">migraine. </a> Migraines are sometimes accompanied by a
visual hallucination superimposed on the real visual scene – often termed a
‘migraine aura’.<o:p></o:p></span></div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjN1BOURyYfQUbJTwuIOwBbtWS4ihdvjqyBTpbutLFMpLSxadKvwfYRIl2F9jJNkQf_U-LrLmF4-8NjfY2Neu4WVXfnjCD-sofROEeSLI2HHOjRpDjkWibluzv1SinHUm-9FGCxmYhVb8uL/s430/migraineAura.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="213" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjN1BOURyYfQUbJTwuIOwBbtWS4ihdvjqyBTpbutLFMpLSxadKvwfYRIl2F9jJNkQf_U-LrLmF4-8NjfY2Neu4WVXfnjCD-sofROEeSLI2HHOjRpDjkWibluzv1SinHUm-9FGCxmYhVb8uL/s320/migraineAura.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small; text-align: start;">A migraine sufferer’s recreation of a ‘migraine aura’</span></td></tr>
</tbody></table>
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<span lang="EN-US"> </span>In migraine or epilepsy, people sometimes
perceive geometric patterns – for example chequer-boards, zig-zag lines, or
concentric rings. These geometric
hallucinations are so consistent across people, they were catalogued in the
1920s by the psychologist <a href="http://www.nap.edu/readingroom.php?book=biomems&page=hkluver.html">Heinrich
Klüver. </a> He divided them into four
types: tunnels and funnels, spirals, lattices, and cobwebs.</div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjPiczjREMhndT9hnWSt65KMfiFqLCsW1jjXvh8hsw7oAHasQg_VYUD82aGr3-WLkJODhC9u6nyPgbSZtUFPs2foAt9lhtu4EevCr_pswdJoicCMEFjYhZKhp0NecIgqbw6vW4TrcgxDOgG/s440/kluverPatterns.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjPiczjREMhndT9hnWSt65KMfiFqLCsW1jjXvh8hsw7oAHasQg_VYUD82aGr3-WLkJODhC9u6nyPgbSZtUFPs2foAt9lhtu4EevCr_pswdJoicCMEFjYhZKhp0NecIgqbw6vW4TrcgxDOgG/s200/kluverPatterns.png" width="158" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small; text-align: start;">Kluver’s four categories of hallucination pattern. <br />Bressloff et al., 2002; used with permission.</span></td></tr>
</tbody></table>
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<span lang="EN-US">These patterned hallucinations are
interesting because they seem to reflect the structure of the parts of the
brain responsible for early visual processing - parts of the brain that are
quite organized in their layout. In the
1970s, mathematicians Jack Cowan and G Ermentrout built models of aberrant
activity patterns, given what they knew about the structure of the visual
cortex. These models have been extended
by the Oxford mathematician<a href="http://www.maths.ox.ac.uk/contact/details/bressloff"> Paul Bressloff</a> </span><!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN PAPERS2_CITATIONS
<citation><uuid>CF42C89E-F6FD-450F-A7F1-A9836F5A9A77</uuid><priority>1</priority><publications><publication><volume>14</volume><publication_date>99200203001200000000220000</publication_date><number>3</number><doi>10.1007/BF00288786</doi><startpage>473</startpage><title>What
Geometric Visual Hallucinations Tell Us about the Visual
Cortex</title><uuid>40B15312-6996-4C95-901A-3A1EAEC3C7E9</uuid><subtype>400</subtype><endpage>491</endpage><type>400</type><url>http://www.mitpressjournals.org/doi/abs/10.1162/089976602317250861</url><bundle><publication><title>Neural
Computation</title><type>-100</type><subtype>-100</subtype><uuid>5B929401-BEA1-478B-9D9C-C314A94725AF</uuid></publication></bundle><authors><author><firstName>Paul</firstName><middleNames>C</middleNames><lastName>Bressloff</lastName></author><author><firstName>Jack</firstName><middleNames>D</middleNames><lastName>Cowan</lastName></author><author><firstName>Martin</firstName><lastName>Golubitsky</lastName></author><author><firstName>Peter</firstName><middleNames>J</middleNames><lastName>Thomas</lastName></author><author><firstName>Matthew</firstName><middleNames>C</middleNames><lastName>Wiener</lastName></author></authors></publication></publications><cites></cites></citation><span
style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US">(Bressloff, Cowan, Golubitsky, Thomas, & Wiener, 2002)</span><!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-end'></span></span><![endif]--><span lang="EN-US">. By modeling unusual
activity in the visual cortex, and then also taking account of the way the
neurons in our visual cortex map onto visual space, these researchers are able
to predict the kind of hallucinatory patterns catalogued by Klüver.<o:p></o:p></span></div>
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjgiOQ3A-8vbkYSh3knvDoTiPwRAkSbLyUAisfohU86yTBZk2mh8G1qOeyxwkypQ_R1_7wzaVffgadfJ3gJMFKPJMF0v2LESj-C1cO-0WBw7b-XunpjbxYzg9S73hnHPSuYsUU7nHmkUies/s433/vortex.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjgiOQ3A-8vbkYSh3knvDoTiPwRAkSbLyUAisfohU86yTBZk2mh8G1qOeyxwkypQ_R1_7wzaVffgadfJ3gJMFKPJMF0v2LESj-C1cO-0WBw7b-XunpjbxYzg9S73hnHPSuYsUU7nHmkUies/s200/vortex.png" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small; text-align: start;">A mathematical simulation of a hallucination pattern</span></td></tr>
</tbody></table>
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<span lang="EN-US">Whilst migraines and epilepsy are certainly
not pleasant, the actual hallucinations experienced are rarely
frightening. The same is true for
Charles Bonnet Syndrome. People
experiencing these hallucinations are usually able to tell them apart from
reality, though sometimes only once they have become used to them and know what
to expect! Of course, this isn’t true of
all hallucinations. Sacks also discusses
the more terrifying types of hallucinations, for example, those of psychosis,
or of the ‘night terror’ associated with sleep paralysis – in which people
awake unable to move, with the feeling that they are trapped beneath a horrible
intruder who is trying to suffocate them (the ‘night mare’ or ‘night hag’).<o:p></o:p></span></div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi_gLojzjmshoOooohZbT2b2YkFxH35nVKSf9bTKY3K48ipLkEgjMYXsI0zukm9LetRwDcA4viixXALyldu0EqlNHba-Kz8INZ6JcGl6z8nWkxYeZ1CqXTYbK1Yle2xfk1Gzm3BZAWkhhJP/s353/nighthag.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="269" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi_gLojzjmshoOooohZbT2b2YkFxH35nVKSf9bTKY3K48ipLkEgjMYXsI0zukm9LetRwDcA4viixXALyldu0EqlNHba-Kz8INZ6JcGl6z8nWkxYeZ1CqXTYbK1Yle2xfk1Gzm3BZAWkhhJP/s320/nighthag.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small; text-align: start;">Nicolai Abildgaard’s ‘Nightmare’</span></td></tr>
</tbody></table>
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<span lang="EN-US">What do these more frightening
hallucinations - in particular, the hallucinations associated with psychosis
(in which people often also experience delusions) - say about the brain? This is a fascinating but difficult area, as
yet poorly understood. Here it becomes
more difficult to draw the line between perceptions and beliefs, and emotional
and motivational factors seem to be more involved. Researchers are currently trying to
understand how hallucinations in diseases like schizophrenia are related to the
other symptoms of the disorder, and how they may be similar or different to the
kind of hallucinations produced by sensory deprivation or epileptic activity
patterns in the brain.<o:p></o:p></span></div>
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<span lang="EN-US">Finally, an interesting speculation that
may haunt you as you read Sacks’ book is that hallucinatory experiences –
which, as Sacks points out, are much more common than one might think – could
be responsible for the religious, mystical, and paranormal parts of our
culture. For example, Sacks points out
that <a href="https://en.wikipedia.org/wiki/Joan_of_Arc">Joan of Arc’s</a>
visions are classic manifestations of epileptic activity in the temporal
lobes. He speculates that these seizure
related visions were the reason an uneducated farmer’s daughter became a
religious leader who rallied thousands of followers.<o:p></o:p></span></div>
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<span lang="EN-US">Sacks’ book is a engrossing survey of
hallucinatory experiences of all types.
In their variety (far more extensive than described in this blog post)
hallucinations provide many insights into the way our ordinary perception works. Reading Sacks’ book is also a good preparation
for the possibility – not too slim, as Sacks points out – that you will one day
have a hallucinatory experience of one form or another (if you haven’t
already!).<o:p></o:p></span></div>
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<b><span lang="EN-US">References<o:p></o:p></span></b></div>
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<!--[if supportFields]><span
lang=EN-US><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN PAPERS2_CITATIONS
<papers2_bibliography/><span style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US">Bressloff, P. C., Cowan, J. D., Golubitsky, M.,
Thomas, P. J., & Wiener, M. C. (2002). What Geometric Visual Hallucinations
Tell Us about the Visual Cortex. <i>Neural Computation</i>, <i>14</i>(3),
473–491. doi:10.1007/BF00288786<o:p></o:p></span></div>
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<br /></div>
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<span lang="EN-US">Merabet, L. B., Maguire, D., Warde, A., Alterescu, K., Stickgold, R.,
& Pascual-Leone, A. (2004). Visual Hallucinations During Prolonged
Blindfolding in Sighted Subjects. <i>Journal of Neuro-Ophthalmology</i>, <i>24</i>(2),
109.<o:p></o:p></span></div>
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<span lang="EN-US">All images Creative Commons except Kluver patterns, from Bresloff et
al., used with permission.<o:p></o:p></span></div>
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StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com1tag:blogger.com,1999:blog-8524853819129983649.post-73304083266054075522013-06-24T07:51:00.000-07:002015-04-24T03:49:53.861-07:00Research Briefing: Dynamic population coding for flexible cognition<br />
<div class="" style="clear: both; text-align: left;">
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjCPzv40dP1bo1mHIyAkHXEG8GY31AI-It-ctUsZCRsTYeR04x1rD1DlnUeOm1K0CAKY9o-k_Ly4huVhFupn1tYyQKTiVmi3gAlsIEIOdtyRxxN8fE6tpwvdk11XCCxvsKWPSKxsqYGY52D/s1600/SVideo4.gif" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjCPzv40dP1bo1mHIyAkHXEG8GY31AI-It-ctUsZCRsTYeR04x1rD1DlnUeOm1K0CAKY9o-k_Ly4huVhFupn1tYyQKTiVmi3gAlsIEIOdtyRxxN8fE6tpwvdk11XCCxvsKWPSKxsqYGY52D/s200/SVideo4.gif" height="200" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Dynamic population coding in prefrontal cortex</td></tr>
</tbody></table>
Our environment is in constant flux. At any given moment there could be a shift in scenario that demands an equally rapid shift in how we interpret the world around us. For example, the meaning of a simple traffic light critically depends on whether you are driving to work or travelling on foot. Our brains must constantly adapt to accommodate an enormous range of such possible scenarios - in this study, we applied new analysis tools to explore how patterns of brain activity change for different task contexts, allowing for flexible cognitive processing (in <a href="http://www.sciencedirect.com/science/article/pii/S0896627313002237" target="_blank">Stokes et al., 2013, Neuron</a>; see also Comment by <a href="http://www.sciencedirect.com/science/article/pii/S0896627313003097" target="_blank">Miller and Fusi in the same issue</a>).</div>
<h4>
Prefontal Cortex</h4>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhWQqN0KFOt2mo0IdM_rFH8ZSKjLW8VAY8t0ZqpsOX2nbfLShrZULBmeZTXGoUstLOTt5e6_CuflS2jv4ennB5R6lmOUvbxUjT27OtrmcYBhiIMXwaTYpC8muszEcU51rK-ZAIqO4ouCbpk/s1600/Fig1.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhWQqN0KFOt2mo0IdM_rFH8ZSKjLW8VAY8t0ZqpsOX2nbfLShrZULBmeZTXGoUstLOTt5e6_CuflS2jv4ennB5R6lmOUvbxUjT27OtrmcYBhiIMXwaTYpC8muszEcU51rK-ZAIqO4ouCbpk/s200/Fig1.png" height="180" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Adapted from Fig 1</td></tr>
</tbody></table>
<br />
We focused our investigation on an area in the frontal lobe known as <a href="http://en.wikipedia.org/wiki/Prefrontal_cortex" target="_blank">lateral prefrontal cortex</a>. This brain area has long been implicated in flexible cognitive processing. Damage to prefrontal cortex is classically associated with reduced cognitive flexibility (<a href="https://en.wikipedia.org/wiki/Alexander_Luria" target="_blank">Luria</a>, 1966) as part of a more general <a href="http://en.wikipedia.org/wiki/Dysexecutive_syndrome" target="_blank">dysexecutive syndrome</a>. In studies using functional magnetic resonance imaging (fMRI), lateral frontal cortex is also usually more active when participants perform tasks that demand cognitive flexibility (<a href="http://www.sciencedirect.com/science/article/pii/S105381190400223X" target="_blank">Wager et al., 2004</a>). It it widely believed that prefrontal cortex is especially important for representing information about our environment and task goals in mind for guiding flexible behaviour (<a href="http://public.wsu.edu/~fournier/Teaching/psych592/Readings/Baddeley_Review_2003.pdf" target="_blank">Baddeley, 2003</a>; <a href="http://www.ncbi.nlm.nih.gov/pubmed/11252769" target="_blank">Miller, 2000</a>).<br />
<h4>
</h4>
<h4>
Dynamic coding population coding</h4>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgYKGRw6e57LAmqHTvYA9r_ZOq1cEkFQ5u0apwSad6fFXpwdyekpUUpd-mIFxq6d2L-AuYJr3OPAWonetEHxAj3OwvkNoZzcR9OaKHysKqmnLdwYfB_K7V8EIE_51aybsr1ZDVzVaptOS6p/s1600/Dynamic+trajectory.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgYKGRw6e57LAmqHTvYA9r_ZOq1cEkFQ5u0apwSad6fFXpwdyekpUUpd-mIFxq6d2L-AuYJr3OPAWonetEHxAj3OwvkNoZzcR9OaKHysKqmnLdwYfB_K7V8EIE_51aybsr1ZDVzVaptOS6p/s200/Dynamic+trajectory.png" height="160" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Dynamic trajectory through state-space</td></tr>
</tbody></table>
<br />
In this study, we observe a highly dynamic process underlying flexible cognitive processing using a statistical approach that allows us to decode the patterns of population-level activity in prefrontal cortex at high temporal resolution. During a task that requires a different stimulus-response mapping according to trial-by-trial instruction cues (see Fig 1), we found that the pattern of activity rapidly changes during processing of the instructive cue stimulus. After this complex cascade through activity state-space (for more info, see <a href="http://www.jneurosci.org/content/31/4/1167.full" target="_blank">Stokes, 2011</a>), overall activity levels return to baseline for the remainder of a delay period spanning the instruction cue and a possible target stimulus.<br />
<br />
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgGIkWhHrTHnHrW3F_rbJZOUgY2nQCqj4CH_npLJMK5qAZEyC5Pugj3eJSqXXsElWkedpY9uY7M5SGQ-sDqMVCWd1C1yQCm5IySd7jVdEiP3MTNMDCK-QiCB5Dl4vUupkrSEE_KRuHTiu_n/s1600/Fig5.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgGIkWhHrTHnHrW3F_rbJZOUgY2nQCqj4CH_npLJMK5qAZEyC5Pugj3eJSqXXsElWkedpY9uY7M5SGQ-sDqMVCWd1C1yQCm5IySd7jVdEiP3MTNMDCK-QiCB5Dl4vUupkrSEE_KRuHTiu_n/s200/Fig5.png" height="90" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Adapted from Fig 5</td></tr>
</tbody></table>
However, the effect of the cue response lingers on. Subsequent stimuli elicit a population response that critically depends on the previous cue identity. In other words, the dynamic population response triggered by the cue stimulus shifts the response profile of the network of prefrontal cells. This shift in tuning profile allows us to decode the current task-rule (i.e., cue indentify) based on a simple driving stimulus (i.e., neutral stimuli, see Fig 5).<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiy2oVRNwp1ctncexYXw_3r9f4xzeVW03coa-7V8cuGpAMwIXfE_xb_6A_NpSGKEmb5e85NtRw7JbFLu-aMRc1IZMKEwLvLiET79MtkwFL3mW-KjNLoWv8dQAPbm7tcr7Yr6xdXmfc93QCV/s1600/Fig6.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiy2oVRNwp1ctncexYXw_3r9f4xzeVW03coa-7V8cuGpAMwIXfE_xb_6A_NpSGKEmb5e85NtRw7JbFLu-aMRc1IZMKEwLvLiET79MtkwFL3mW-KjNLoWv8dQAPbm7tcr7Yr6xdXmfc93QCV/s200/Fig6.png" height="142" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Adapted from Fig 6</td></tr>
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More importantly, the shift in the network response profile could also underlie task-dependent target processing (i.e., choice stimuli, see Fig 6). The population response to potential target stimuli rapidly evolved from a stimulus-specific coding scheme, to a more abstract code that distinguishes only between different target and non-target items. This dynamic tuning property is ideal for flexible cognition (<a href="http://www.cnbc.cmu.edu/~tai/readings/nature/duncan_code_prefrontal.pdf" target="_blank">Duncan, 2001</a>).<br />
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<h4>
Putative mechanism: flexible connectivity</h4>
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The flow of brain activity critically depends on the pattern of connections between neurons. Contrary to intuition, these connections are always changing. The pattern of connections that make up the very essence of personal experience is constantly adjusting and adapting to the myriad changes experienced throughout life.<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiWScCfEPgUwx9l88Icra9ffr8cAIJGrDyUcjKtxuq_jo4ACxKsqNElb25txDIf74m-T-sDmHI9a7IpMrWbOpykxc2gJf37sIEsGFNMF0n3VJN8-kS4mTeEoCalIk_Go4J9xAzCRjkGT7Vr/s1600/synaptic+plasticity.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiWScCfEPgUwx9l88Icra9ffr8cAIJGrDyUcjKtxuq_jo4ACxKsqNElb25txDIf74m-T-sDmHI9a7IpMrWbOpykxc2gJf37sIEsGFNMF0n3VJN8-kS4mTeEoCalIk_Go4J9xAzCRjkGT7Vr/s200/synaptic+plasticity.jpg" height="200" width="160" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Synaptic Plasticity [<a href="http://en.wikipedia.org/wiki/Neuron" target="_blank">wiki commons</a>]</td></tr>
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Extensive research focuses on long-term structural changes in connectivity through <a href="http://www.scholarpedia.org/article/Models_of_synaptic_plasticity" target="_blank">synaptic plasticity</a>, however the rapid changes we experience from moment-to-moment requires a more flexible kind of memory that can represent the transient features of a given scenario. This kind of flexible "online" memory is typically referred to as ‘<a href="http://www.scholarpedia.org/article/Working_memory" target="_blank">working memory</a>’.<br />
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It has long been assumed that working memory is maintained by keeping a specific thought in mind, like a static snapshot of a visual image or an abstract goal such as ‘turn left at the next set of lights’. However, more recent evidence suggests that working memory can also be stored by laying down specific, but temporary neural pathways (e.g., <a href="http://www.sciencemag.org/content/319/5869/1543.long" target="_blank">Mongillo, Barak & Tsodyks, 2008</a>). Neural pathways are formed by synaptic connections. In a comprehensive review of the literature on short-term synaptic plasticity, <a href="https://mcb.berkeley.edu/labs/zucker/PDFs/Zucker_AnnuRevNeurosci12,13.pdf" target="_blank">Zucker</a> (1989) writes: “Chemical synapses are not static. Postsynaptic potentials wax and wane, depending on the recent history of presynaptic activity”. Short-term plasticity could provide a key mechanisms for flexible connectivity that is necessary for rapid, but temporary changes in network behaviour.<br />
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This new idea allows for a more dynamic theory of brain function, which is more consistent with the everyday experience of continuous thought processes that seem to evolve through time, rather than persist as a static representation. We suggest that short-term plasticity could help explain our data:<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhGgpfoz7q8VVBcSf0yqmH_31V-6X068SWugkUEiak2K20fAXzlWV6xOytj8f1URn3SGBL-G3ZbcNa-RqkYLHnMOSCx44ILddI2bhszhXP535nKGgx_oTuSk3kDn5D8XeltRiQXELJnZDDm/s1600/Fig7.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhGgpfoz7q8VVBcSf0yqmH_31V-6X068SWugkUEiak2K20fAXzlWV6xOytj8f1URn3SGBL-G3ZbcNa-RqkYLHnMOSCx44ILddI2bhszhXP535nKGgx_oTuSk3kDn5D8XeltRiQXELJnZDDm/s320/Fig7.png" height="108" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Adapted from Fig 7</td></tr>
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The initial instruction cue stimulus establishes a specific (but temporary) connectivity state during the most active phase of the response. This would explain why the pattern constantly changes - if the synapse are constantly changing, then even identical input to the system will result in constantly shifting output patterns (<a href="http://www.nature.com/nrn/journal/v10/n2/abs/nrn2558.html" target="_blank">Buonomano and Maass, 2009</a>). This temporary shift in the response sensitivity of the prefrontal network allows the identity of previous input to be decoded by the patterned response to subsequent input, consistent with the silent memory hypothesis. Finally, dynamic changes in connectivity could also be used to rapidly shift the tuning profile of the prefrontal network to accommodate changes in what specific stimuli mean for behaviour (see Fig. 7).<br />
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Broader implications</h4>
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Brain activity is inherently non-stationary - the continuity/stability of cognitive states are unlikely to depend on static activity states, but rather rapid changes in temporary connectivity patterns. This research also raises the intriguing possibility that cognitive capacity limits are not so much constrained by the sheer amount of information that we can keep in mind, but rather how we can put that information to use. Further research in our lab will explore these exciting possibilities.<br />
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Reference:<br />
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Stokes, Kusunoki, Sigala, Nili, Gaffan and Duncan (2013). Dynamic Coding for Cognitive Control in Prefrontal Cortex. Neuron, 78, 364-375 [<a href="http://www.cell.com/neuron/abstract/S0896-6273(13)00223-7" target="_blank">here</a>]<br />
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Also see coverage: <a href="https://www.earlkmiller.org/2013/04/25/dynamic-coding-for-cognitive-control-in-prefrontal-cortex/" target="_blank">Miller Lab</a> (MIT), <a href="http://www.earlkmiller.org/wp-content/uploads/2013/04/Miller-and-Fusi-Neuron-Preview-April-2013.pdf" target="_blank">Neuron Preview</a><br />
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Other literature cited:<br />
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Baddeley, A. (2003). Working memory: looking back and looking forward. Nat. Rev. Neurosci. 4, 829–839. [<a href="http://public.wsu.edu/~fournier/Teaching/psych592/Readings/Baddeley_Review_2003.pdf" target="_blank">here</a>]<br />
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Buonomano, D.V., and Maass, W. (2009). State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10, 113–125. [<a href="http://www.nature.com/nrn/journal/v10/n2/abs/nrn2558.html" target="_blank">here</a>]<br />
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Luria, A.R. (1966). Higher Cortical Functions in Man (New York: Basic Books).<br />
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Miller, E.K. (2000). The prefrontal cortex and cognitive control. Nat. Rev. Neurosci. 1, 59–65. [<a href="http://www.ncbi.nlm.nih.gov/pubmed/11252769" target="_blank">here</a>]<br />
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Mongillo, G., Barak, O., and Tsodyks, M. (2008). Synaptic theory of working memory. Science 319, 1543–1546. [<a href="http://www.sciencemag.org/content/319/5869/1543.long" target="_blank">here</a>]<br />
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Wager, T.D., Jonides, J., and Reading, S. (2004). Neuroimaging studies of shifting attention: a meta-analysis. Neuroimage 22, 1679–1693. [<a href="http://www.sciencedirect.com/science/article/pii/S105381190400223X" target="_blank">here</a>]<br />
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Zucker (1989) Short-term synaptic plasticity. Ann. Rev. Neurosci, 12: 13-31 [<a href="https://mcb.berkeley.edu/labs/zucker/PDFs/Zucker_AnnuRevNeurosci12,13.pdf" target="_blank">here</a>]<br />
<br />StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-78691363608468178442013-06-23T15:09:00.000-07:002013-06-25T10:35:10.779-07:00Neuroscience can reveal mysteries of the mind<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgpCzCp8Yh0HbppacPRyuGsObsm198lDaIyPwpnVRFu2JUL5Umscd5CxVOj1s2oi_2XHbvbXkG-6FGXre1fGcvcdl4oByvQIeZkb0WIU6Dvbu4kgJRTwBxrXKaBYwwQVXsayOmpt693f7Gx/s1600/Cartesian+Dualism.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgpCzCp8Yh0HbppacPRyuGsObsm198lDaIyPwpnVRFu2JUL5Umscd5CxVOj1s2oi_2XHbvbXkG-6FGXre1fGcvcdl4oByvQIeZkb0WIU6Dvbu4kgJRTwBxrXKaBYwwQVXsayOmpt693f7Gx/s200/Cartesian+Dualism.jpg" width="161" /></a></div>
Recently I responded in the <a href="http://www.guardian.co.uk/science/blog/2013/jun/25/neuroscience-media-neuromania?CMP=twt_fd">Guardian </a>to a couple of high-profile articles criticising over-hyped neuroscience [e.g., <a href="http://www.guardian.co.uk/commentisfree/2013/jun/02/brain-scans-innermost-thoughts" target="_blank">here</a> and <a href="http://www.nytimes.com/2013/06/18/opinion/brooks-beyond-the-brain.html?_r=0" target="_blank">here</a>]. Most of the claims were levelled at bad scientific practices identified in the field, however boldly concluded that neuroscience is <i>in principle</i> unable to answer deep questions of mind.<br />
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In my response, I point out that current limitations <i>in practice</i> do not imply limitations <i>in principle</i>. It is far too early to predict so-called "in principle limits". Also, I point out that the neurocentric view does not necessarily neglect all the external (non-brain) influences that shape our experience (i.e., society, culture, history, art, etc). The goal for neuroscience is to understand how the brain responds to all such influences, from basic sensory stimulation to social and cultural influences. Finally, I also make the point that neuroscience is not just functional magnetic resonance imaging (<a href="http://en.wikipedia.org/wiki/Functional_magnetic_resonance_imaging" target="_blank">fMRI</a>), and the <a href="http://theconversation.com/adventures-in-blobology-20-years-of-fmri-brain-scanning-4095">blobology</a> often used to parody fMRI, and neuroscience by association. Neuroscience is a multilevel approach that includes a vast array of complementary techniques, which is often neglected by <i>in principle</i> critics of neuroscience who tend to focus on the more simplistic, and sensationalist, claims that circulate around the mainstream media. Similar responses have been elicited elsewhere [<a href="http://neurocritic.blogspot.co.uk/2013/06/all-washed-up.html" target="_blank">Neurocritic</a>, <a href="http://bjoern.brembs.net/2013/06/free-will-is-not-what-you-think/" target="_blank">Brembs</a>, <a href="http://www.newyorker.com/online/blogs/elements/2013/06/the-problem-with-the-neuroscience-backlash.html" target="_blank">New Yorker</a>, <a href="http://blog.brainfacts.org/2013/06/david-brooks-doesnt-understand-neuroscience/#.UcmjZPmkrVZ">BrainFacts.org</a>]. In this post, I would just like to elaborate on the more general question of mind, and what we might expect neuroscience to help us understand.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhT0Xx7mMdHIrP67rPAT9NeT5-f333T6LYqCZL07saJU7Pdo7vYxlt-FPmhnnGHbKn0iZiYZ80Ny1IzozMJvmX0Cz1jBiU8cONJqWtHYd4dBRBDfkujHrmbU0i9aZIBoQtoFsXLI1Dsyndv/s1600/The+Concept+of+Mind+Ryle.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhT0Xx7mMdHIrP67rPAT9NeT5-f333T6LYqCZL07saJU7Pdo7vYxlt-FPmhnnGHbKn0iZiYZ80Ny1IzozMJvmX0Cz1jBiU8cONJqWtHYd4dBRBDfkujHrmbU0i9aZIBoQtoFsXLI1Dsyndv/s200/The+Concept+of+Mind+Ryle.jpg" width="134" /></a>Recently, philosopher, poet, novelist and cultural critic <a href="http://www.guardian.co.uk/profile/raymond-tallis" target="_blank">Raymond Tallis</a> reminds us <a href="http://www.guardian.co.uk/commentisfree/2013/jun/02/brain-scans-innermost-thoughts" target="_blank">the brain is not the mind</a> [see <a href="http://www.nytimes.com/2013/06/18/opinion/brooks-beyond-the-brain.html?_r=0" target="_blank">here</a> for a similar argument by <a href="http://en.wikipedia.org/wiki/David_Brooks_(journalist)" target="_blank">David Brooks</a> in the NY Times]. As Gilbert <a href="https://en.wikipedia.org/wiki/Gilbert_Ryle" target="_blank">Ryle</a> famously argues in the Concept of Mind, to confuse the two levels of description is to commit a category mistake. The brain is not the mind, but the basic medium that gives rise to all mental faculties. In other words, mind is what the brain does. The philosophical distinction between mind and brain is valid and important, but it does not imply any limit on how much studying the brain will inform us about the workings of the mind. That is an empirical question. </div>
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<h4>
Experience - The Explanatory Gap</h4>
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhV-XilxxplaxPZ2UauDaaTXjFDq9P4_L38Y7Bc8Pn4yLzQfe7_5K0tR3FYO85rPa-_7brKiwugsNnW5jhggXBTMTkJjODVbK77V5er96RgPmXdCryTE4Rv7oNHPKRg02-3kPhTsWI6z6p7/s1600/Mind+the+explanatory+gap.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhV-XilxxplaxPZ2UauDaaTXjFDq9P4_L38Y7Bc8Pn4yLzQfe7_5K0tR3FYO85rPa-_7brKiwugsNnW5jhggXBTMTkJjODVbK77V5er96RgPmXdCryTE4Rv7oNHPKRg02-3kPhTsWI6z6p7/s200/Mind+the+explanatory+gap.jpg" width="198" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">From <a href="http://commons.wikimedia.org/wiki/File:Cheltenham_..._MIND_THE_GAP_(6191889892).jpg" target="_blank">Wiki Commons</a></td></tr>
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I think some of the confusion comes from different ideas of knowledge: explanation vs. experience. To quote from another famous philosophical example, <a href="https://en.wikipedia.org/wiki/Frank_Cameron_Jackson" target="_blank">Frank Jackson</a> considers the plight of Mary the colour scientist. She "knows all the physical facts about colour, including every physical fact about the experience of colour in other people, from the behavior a particular colour is likely to elicit to the specific sequence of neurological firings that register that a colour has been seen. However, she has been confined from birth to a room that is black and white, and is only allowed to observe the outside world through a black and white monitor. When she is allowed to leave the room, it must be admitted that she learns something about the colour red the first time she sees it — specifically, she learns what it is like to see that colour" [from <a href="http://www.sfu.ca/~jillmc/JacksonfromJStore.pdf" target="_blank">here</a>].</div>
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But this is a red herring - who really expects neuroscience to substitute subjective experience? If you want to experience red, you should find something red to look at. If you want to experience Bach, then go to a concert and leave the neuroscientists alone! You will certainly learn something new that can't be got from reading all the research on how the brain processes colour or music. If you do not have the basic neural machinery necessary for these experiences, then you will remain ever-deprived in this respect as no other kind of knowledge will substitute for experience. Neuroscience (or any other study) is never going to provide a satisfactory substitute for direct subjective experience, but if you are searching for a causal explanation <i>how </i>the brain gives rise to these experiences, then there is no substitute for neuroscience.</div>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjcAO8qqStRgwHpViZhHE2LWdXR5StDOMoVmREZAP45qUf_vTlZDEqoop6Mf-Z-jB98i5ZFpaP3rRadOMKFgbzE4IH7_-XkD1dhEjRbmD9NX-QzPx8ZM7GLgDymZMBh3Jt9cHSgfQcgPhH7/s1600/Rotating_brain.gif" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjcAO8qqStRgwHpViZhHE2LWdXR5StDOMoVmREZAP45qUf_vTlZDEqoop6Mf-Z-jB98i5ZFpaP3rRadOMKFgbzE4IH7_-XkD1dhEjRbmD9NX-QzPx8ZM7GLgDymZMBh3Jt9cHSgfQcgPhH7/s200/Rotating_brain.gif" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><a href="http://commons.wikimedia.org/wiki/File:Rotating_brain.gif?uselang=en-gb" target="_blank">Wiki Commons</a></td></tr>
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Every experience we have, every memory, every perception, hope, dream, plan, action... everything that makes up our mental life is causally dependent on some electrochemical state in the brain. In the modern age, this basic <a href="http://en.wikipedia.org/wiki/Materialism" target="_blank">materialist </a>view is rarely contested, even by the most vociferous critics of neuroscience (though Brooks gets pretty close <a href="http://www.nytimes.com/2013/06/18/opinion/brooks-beyond-the-brain.html" target="_blank">here</a>). It is simply no longer credible to invoke some non-material entity (<a href="http://www.blogger.com/"><span id="goog_1473879845"></span>ghost in the machine<span id="goog_1473879846"></span></a>) as the ultimate cause of the private and uniquely special quality of human experience. If we want to understand how the material of brain gives rise to the phenomena of mind, then we need to understand the causal biological mechanisms that underpin the cognitive architecture that is collectively termed mind. This includes perception, memory, imagination, language (and other social interactions), sense of agency/free will, etc. But I reiterate, the purpose is to understand the <i>causal mechanisms</i> the give rise to the phenomena of mind, not to substitute the first order experience. The explanatory gap is simply a red herring.</div>
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I have argued that it is too early to predict how far neuroscience will be able to take us. It is hard to imagine some magical endpoint at which the final piece of the puzzle falls into place, and all mystery finally dissolves. But there is every reason to believe that the current direction is a promising one, and new technical developments and analysis approaches are likely to yield important new insights that can hardly be predicted at this early stage of the adventure. But to make a case in favour of neuroscience as a likely best place to look for answers to mind, it makes sense to consider how far we have come so far.<br />
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<h4>
Never mind the neurobollocks</h4>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjlgv8o0RPbXkm2DRipGV54wCX1eAxKqgTfIUpzXEiIzl-pmZtFE1-m_dv7N6bGCBmx_G-TjzjfPrh_tudEiVotKWUGcXlxVNjuRuqra6EF8VaCa4xllyKfaMCGlNNwYfs-UTswxSCifsoA/s1600/Phrenology.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjlgv8o0RPbXkm2DRipGV54wCX1eAxKqgTfIUpzXEiIzl-pmZtFE1-m_dv7N6bGCBmx_G-TjzjfPrh_tudEiVotKWUGcXlxVNjuRuqra6EF8VaCa4xllyKfaMCGlNNwYfs-UTswxSCifsoA/s200/Phrenology.jpg" width="160" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Phrenology (<a href="http://commons.wikimedia.org/wiki/File:PhrenologyPix.jpg?uselang=en-gb" target="_blank">Wiki Commons</a>)</td></tr>
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As neurobacklashers are quick to point out, there have been many examples of over-hyped studies (usually some form of one-to-one mapping between cognitive states X and Y to brain areas A and B using fMRI). Neurophrenology is impossibly simplistic and theoretically absurd, but it is probably an important first step to map out some of the basic correspondences between brain structure and function before we can move on to more complex interactions. The neurobacklash may also draw upon some more systemic problems with the practical application of neuroscience (e.g., poor statistical methods, unreliable results, etc). These are all serious problems in the field today, but also not unique to neuroscience. In fact, one of the most striking and often cited examples of such bad practices comes from preclinical cancer research, which found that only about 10% of previously published results were reliable, with the implication that implying that almost 90% of results published in preclinical cancer research were <a href="http://www.nature.com/nature/journal/v483/n7391/full/483531a.html">effectively false positives</a>.</div>
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All scientific conclusions depend critically on the rigour of the scientific practice that is used to gather and evaluate the evidence. I have previously argued that current funding models prioritise quantity over quality [posts at the <a href="http://www.guardian.co.uk/science/blog/2013/apr/16/folly-science-shoestring" target="_blank">Guardian </a>and <a href="http://the-brain-box.blogspot.co.uk/2013/04/statistical-power-is-truth-power.html" target="_blank">Brain Box</a>], which seriously distorts the incentive structure in science to reward shoddy practices for expedient publications. It is the same if your building contractor cuts corners to save on costs. An unstable edifice built on under-resource science will not stand the test of time. Worse, science is a cumulative process, so poor science leads future research down blind alleys. I have also advocated more stringent statistical criteria [<a href="http://the-brain-box.blogspot.co.uk/2012/09/must-we-really-accept-1-in-20-false.html" target="_blank">Brain Box</a>], and others have argued for more checks and balances in the publication process [<a href="http://blogs.discovermagazine.com/neuroskeptic/2013/05/27/fixing-science-not-just-psychology/" target="_blank">here</a>, <a href="http://www.guardian.co.uk/science/blog/2013/jun/05/trust-in-science-study-pre-registration" target="_blank">here</a>]. We must remain ever-vigilant to protect previously established safeguards from increasing pressure to cut-corners, and also find new ways to improve the reliability of established results. </div>
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Although a litany of bad practices in neuroscience does not imply that the endeavour is flawed in principle, it would undermine the future promise if there were no examples that survived the <i>in practice</i> critique. But this is simply not the case. Neuroscience has completely revolutionised our understanding of many core mental faculties over the last century. Research in long-term memory is probably a good example to illustrate how neuroscience can provide a powerful explanatory framework for understanding how the biology of brain causes a key mental faculty.<br />
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Case study: Long-term Memory</h4>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh4eOkIS4epXbF2uw6SEcT-HZTGzeDar7Pb7vgs0DMrgvf9xjyvIhyphenhyphenbaDwVgKmMkwGwn5rzASwFvzjMETUoG_Ms8g4QoVtK5GRIgCO32yMt25klsS_BQMsv5dO8WdDwf3mv4t8rhL21caYm/s1600/hippocampus.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img alt="" border="0" height="151" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh4eOkIS4epXbF2uw6SEcT-HZTGzeDar7Pb7vgs0DMrgvf9xjyvIhyphenhyphenbaDwVgKmMkwGwn5rzASwFvzjMETUoG_Ms8g4QoVtK5GRIgCO32yMt25klsS_BQMsv5dO8WdDwf3mv4t8rhL21caYm/s200/hippocampus.png" title="Hippocampus" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">From <a href="http://en.wikipedia.org/wiki/Hippocampus" target="_blank">Wiki</a></td></tr>
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This story starts with the (in)famous case of an ill-fated surgical procedure to treat otherwise intractable <a href="http://en.wikipedia.org/wiki/Epilepsy" target="_blank">epilepsy</a>. After bilateral resection of the <a href="http://en.wikipedia.org/wiki/Medial_temporal_lobe" target="_blank">medial temporal lobe </a>in a patient widely known by his initial <a href="http://en.wikipedia.org/wiki/Henry_Molaison" target="_blank">HM</a>, we discovered that a very specific part of the brain was absolutely necessary for long-term memory: the <a href="http://en.wikipedia.org/wiki/Hippocampus" target="_blank">hippocampus</a>. This was a remarkable case of localisation. Without the hippocampus, the patient becomes profoundly <a href="https://en.wikipedia.org/wiki/Anterograde_amnesia" target="_blank">amnesic</a>, therefore we can conclude that this brain structure is necessary for forming new memories. But not all types of memories. The amnesic patient is still able to learn new motor skills, for example, so we learn something important about the mind - there are different types of memory [see <a href="http://www.scholarpedia.org/article/Memory" target="_blank">here</a> for other examples]. Moreover, the patient is also able to recall old memories, suggesting that our past experiences are stored in widely distributed networks throughout the entire brain (massively non-localised). </div>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhfNfp512ZtDoEOnL7nnQWP2tgU2Dxf_oYiZRxi7Ys5YXkJ2icygDxuAvgRQo7TCO-q28ffqSPcHxOyoifVN6UHG-5kajIP2WkWxhVZ3GijzcgKAc_DmASp6PSTpuctnHB8FQCoAhYYdpdC/s1600/MRI.JPG" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="150" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhfNfp512ZtDoEOnL7nnQWP2tgU2Dxf_oYiZRxi7Ys5YXkJ2icygDxuAvgRQo7TCO-q28ffqSPcHxOyoifVN6UHG-5kajIP2WkWxhVZ3GijzcgKAc_DmASp6PSTpuctnHB8FQCoAhYYdpdC/s200/MRI.JPG" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">MRI (<a href="http://commons.wikimedia.org/wiki/File:Modern_3T_MRI.JPG?uselang=en-gb" target="_blank">Wiki Commons</a>)</td></tr>
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So the further neuroscientific question naturally arises: how do we form new memories? One popular theory is that the hippocampus re-activates recent experiences during sleep. This results in a kind of replay sequence of events is thought to eventually re-wire existing networks in the rest of the brain to integrate the new experience with all the other previous experiences. This is perhaps why we have experiences in our sleep (albeit with confusing/disjointed narratives). Unfortunately, dreams are notoriously difficult to study. There are no observable behaviours for animal studies (other than a bit of paw twitching... we can't ask a mouse to keep a dream diary), and human studies must rely on whatever experience survives the transition between sleep and wakefulness. Neuroscience is able to break through this barrier by measuring patterns of activity during sleep. Already, we have seen how activity in the mouse hippocampus reflects reactivation of previous experiences of recent events [i.e., learning a new maze: <a href="http://www.nature.com/neuro/journal/v11/n2/abs/nn2037.html" target="_blank">article</a>]. More exciting, a recent proof-of-principle fMRI study has now shown that it won't be long before we can extend the same approach to humans [article <a href="http://www.sciencemag.org/content/340/6132/639.abstract" target="_blank">here</a>, and my review <a href="http://the-brain-box.blogspot.co.uk/2013/04/in-news-decoding-dreams-with-fmri.html" target="_blank">here </a>and <a href="http://www.nature.com/scitable/blog/student-voices/dream_catcher_the_neuroscience_behind" target="_blank">here</a>]. Aside from the general awe and wonder associated with idea that we can reading peoples brains during sleep, these kind of studies provide the basic pathways to entirely new approaches for understanding the mental rumblings that are beyond the scope of other forms of enquiry [see here on '<a href="http://the-brain-box.blogspot.co.uk/2012/06/real-mind-reading.html" target="_blank">mind reading</a>'].<br />
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But this is just one example how neuroscience has provided key insights in the fundamental mechanisms of mind: memory (see <a href="http://bjoern.brembs.net/2013/06/free-will-is-not-what-you-think/?utm_source=buffer&utm_campaign=Buffer&utm_content=buffer94a0a&utm_medium=twitter" target="_blank">here</a> for a related discussion on free will by Björn <a href="http://brembs.net/" target="_blank">Brembs</a>). If this level of explanation does not satisfy your definition of "learning something new about the mind", then I can't imagine any other form of enquiry that is likely to be more satisfactory. We have not learned <i>what it is like</i> to have memory (i.e., the explanatory gap), but most of us already know what memory feels like anyway. The deeper question is how the brain gives rise to such phenomena of mind. Some questions of mind are amenable to introspection, others can be studied using more subtle cognitive behavioural experiments (i.e., cognitive psychology), while others can only be realistically addressed using neuroscientific methods. Future developments in neuroscientific methods will set the limit of this endeavour.<br />
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For other excellent (err... like-minded) responses to recent neurobacklash: <a href="http://neurocritic.blogspot.co.uk/2013/06/all-washed-up.html" target="_blank">Neurocritic Blog</a>, Björn <a href="http://bjoern.brembs.net/2013/06/free-will-is-not-what-you-think/" target="_blank">Brembs Blog</a>, <a href="http://www.newyorker.com/online/blogs/elements/2013/06/the-problem-with-the-neuroscience-backlash.html" target="_blank">New Yorker</a>, <a href="http://blog.brainfacts.org/2013/06/david-brooks-doesnt-understand-neuroscience/#.UcmjZPmkrVZ">BrainFacts.org</a>]. </div>
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StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com1tag:blogger.com,1999:blog-8524853819129983649.post-91166036110987751222013-04-23T14:43:00.000-07:002013-04-23T23:56:24.454-07:00In the news: Decoding dreams with fMRI<div class="separator" style="clear: both; text-align: center;">
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Recently Horikawa and colleagues from <a href="http://www.cns.atr.jp/dni/en/" target="_blank">ATR Computational Neuroscience Laboratories,</a> in Kyoto (Japan), caused a media sensation with the publication of the study in <a href="http://www.sciencemag.org/content/early/2013/04/03/science.1234330.abstract" target="_blank">Science</a> that shows first-time proof-of-principle that non-invasive brain scanning (<a href="http://en.wikipedia.org/wiki/Functional_magnetic_resonance_imaging" target="_blank">fMRI</a>) can be used to decode dreams. Rumblings were already heard in various media circles after <a href="http://www.cns.atr.jp/dni/en/members/kamitani_e/" target="_blank">Yuki Kamitani </a>presented their initial findings at the annual meeting of the Society for Neuroscience in New Orleans last year [see Mo Costandi's <a href="http://www.nature.com/news/scientists-read-dreams-1.11625" target="_blank">report</a>]. But now the peer-reviewed paper is officially published, the press releases have gone out and the journal embargo has been lifted, there was a media frenzy [e.g., <a href="http://www.bbc.co.uk/news/science-environment-22031074" target="_blank">here</a>, <a href="http://www.wired.com/wiredscience/2013/04/dream-decoder/" target="_blank">here </a>and <a href="http://www.npr.org/blogs/health/2013/04/04/176224026/researchers-use-brain-scans-to-reveal-hidden-dreamscape" target="_blank">here</a>]. The idea of reading people's dreams was always bound to attract a lot of media attention. <br />
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OK, so this study is cool. OK, very cool - what could be cooler than reading people's dreams while they sleep!? But is this just a clever parlour trick, using expensive brain imaging equipment? What does it tell us about the brain, and how it works?<br />
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First, to get beyond the hype, we need to understand exactly what they have, and have not, achieved in this study. Research participants were put into the narrow bore of an fMRI for a series of mid afternoon naps (up to 10 sessions in total). With the aid of <a href="http://en.wikipedia.org/wiki/EEG-fMRI" target="_blank">simultaneous EEG</a> recordings, the researchers were able to detect when their volunteers had slipped off into the earliest stage of sleep (stage 1 or 2). At this point, they were woken and questioned about any dream that they could remember, before being allowed to go back to sleep again. That is, until the EEG next registered evidence of early stage sleep again, and then again they were awoken, questioned, and allowed back to sleep. So on and so forth, until they had recorded at least 200 distinct awakenings.<br />
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After all the sleep data were collected, the experimenters then analysed the verbal dream reports using a semantic network analysis (WordNet) to help organise the contents of the dreams their participants had experience during the brain scans. The results of this analysis could then be used to systematically label dream content associated with the sleep-related brain activity they had recorded earlier.<br />
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Having identified the kind of things their participants had been dreaming about in the scanner, the researchers then searched for actual visual images that best matched the reported content of dreams. Scouring the internet, the researchers built up a vast database of images that more or less corresponded to the contents of the reported dreams. In a second phase of the experiment, the same participants were scanned again, but this time they were fully awake and asked to view the collection of images that were chosen to match their previous dream content. These scans provided the research team with individualised measures of brain activity associated with specific visual scenes. Once these patterns had been mapped, the experimenters returned to the sleep data, using the normal waking perception data as a reference map.<br />
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If it looks like a duck...</h4>
In the simplest possible terms, if the pattern of activity measured during one dream looks more like activity associated with viewing a person, compared to activity associated with seeing an empty street scene, then you should say that the dream probably contains a person, if you were forced to guess. This is the essence of their decoding algorithm. They use sophisticated ways to characterise patterns in fMRI activity (<a href="http://en.wikipedia.org/wiki/Support_vector_machine" target="_blank">support vector machine</a>), but essentially the idea is simply to match up, as best they can, the brain patterns observed during sleep with those measures during wakeful viewing of corresponding images. Their published result is shown on the right for different areas of the brain's visual system. Lower visual cortex (LVC) includes primary visual cortex (V1), and areas V2 and V3; whereas higher visual cortex (HVC) includes lateral occipital complex (LOC), fusiform face area (FFA) and parahippocampal place area (PPA).<br />
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Below is a more creative reconstruction of this result. The researchers have put together a movie based on one set of sleep data taken before waking. Each frame represents the visual image from their database that best matches the current pattern of brain activity. Note, the reason why the image gets clearer towards the end of the movie is because the brain activity is nearer to the time point at which the participants were woken, and therefore were more likely to be described at waking. If the content at other times did not make it into the verbal report, then the dream activity would be difficult to classify because the corresponding waking data would not have been entered into the image database. This highlights how this approach only really works for content that has been characterised using the waking visual perception data. <br />
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<iframe allowfullscreen='allowfullscreen' webkitallowfullscreen='webkitallowfullscreen' mozallowfullscreen='mozallowfullscreen' width='320' height='266' src='https://www.blogger.com/video.g?token=AD6v5dxeBi6WiGHmBROcBqpq5VF9IH3t6Sfi-AzL_wJuf4F0ItE9UsSSJPjP4BEHVeJdjWFIjwWyqu7-N89SlyqouQ' class='b-hbp-video b-uploaded' frameborder='0'></iframe>OK, so these scientists have decoded dreams. The accuracy is hardly perfect, but still, the results are significantly above chance, and that's no mean feat. In fact, it has never been done before. But some might still say, so what? Have we learned anything very new about the brain? Or is this just a lot of neurohype?<br />
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Well, beyond the tour de force technical achievement of actually collecting this kind of multi-session simultaneous fMRI/EEG sleep data, these results also provide valuable insights into how dreams are represented in the brain. As in many neural decoding studies, the true purpose of the classifier is not really to make perfectly accurate predictions, but rather to work out how the brain represented information by studying how patterns of brain activity differ between conditions [see previous <a href="http://the-brain-box.blogspot.co.uk/2012/06/real-mind-reading.html" target="_blank">post</a>]. For example, are there different patterns of visual activity during different types of dreams? Technically, this could be tested by just looking for any difference in activity patterns associated with different dream content. In <a href="http://en.wikipedia.org/wiki/Machine_learning" target="_blank">machine-learning</a> language, this could be done using a <a href="http://en.wikipedia.org/wiki/Cross-validation_(statistics)" target="_blank">cross-validated classification </a>algorithm. If a classifier trained to discriminate activity patterns associated with known dream states can then make accurate predictions of new dreams, then it is safe to assume that there are reliable differences in activity patterns between the two conditions. However, this only tells you that activity in a specific brain area is different between conditions. In this study, they go one step further.<br />
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By training the dream decoder using only patterns of activity associated with the visual perception of actual images, they can also test whether there is a systematic relationship between the way dreams are presented, and how actual everyday perception is represented in the brain. This cross-generalisation approach helps isolate the shared features between the two phenomenological states. In my own research, we have used this approach to show that visual imagery during normal waking selectively activates patterns in high-level visual areas (lateral occipital complex: LOC) that are very similar to the patterns associated with directly viewing the same stimulus (<a href="http://www.jneurosci.org/content/29/5/1565.full.pdf" target="_blank">Stokes et al., 2009, J Neurosci</a>). The same approach can be used to test for other coding principles, including high-order properties such as position-invariance (<a href="http://www.ncbi.nlm.nih.gov/pubmed/21376815" target="_blank">Stokes et al., 2011, NeuroImage</a>), or the pictorial nature of dreams, as studied here. As in our previous findings during waking imagery, Horikawa et al show that the visual content of dreams shares similar coding principles to direct perception in higher visual brain areas. Further research, using a broader base of comparisons, will provide deeper insights into the representational structure of these inherently subject and private experiences.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgTY7Uz1rsC1VYWKnNCGvkv7mBbDWl5wkTlGOfsnLfw7cdAH-W4seb146mbCUdGrwukMxB1tDpM4J9L1wiy87rOkeS9m0GId_vJuILrTid38SaA3QTLTZilfgFhAP5CM5yE5NxP5y1DyowL/s1600/ghostbuster+brain+reader.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgTY7Uz1rsC1VYWKnNCGvkv7mBbDWl5wkTlGOfsnLfw7cdAH-W4seb146mbCUdGrwukMxB1tDpM4J9L1wiy87rOkeS9m0GId_vJuILrTid38SaA3QTLTZilfgFhAP5CM5yE5NxP5y1DyowL/s200/ghostbuster+brain+reader.jpg" width="149" /></a>Many barriers remain for an all-purpose dream decoder</h4>
When the media first picked up this story, the main question I was asked went something like: are scientists going to be able to build dream decoders? In principle, yes, this result shows that a well trained algorithm, given good brain data, is able to decode the some of the content of dreams. But as always, there are plenty of caveats and qualifiers.<br />
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Firstly, the idea of downloading people's dreams while they sleep is still a very long way off. This study shows that, in principle, it is possible to use patterns of brain activity to infer the contents of peoples dreams, but only at a relatively coarse resolution. For example, it might be possible to distinguish between patterns of activity associated with a dream containing people or an empty street, but it is another thing entirely to decode which person, or which street, not to mention all the other nuances that make dreams so interesting.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhqh_YcxEyeE0jVNeKvdUy9lZIzAL8KCTWjS9KzUHvF0P58-bUD0EUgvviY59p1jecwe3Ktig1fANWZjh704LyOD4n2GN7SibIwdD7eNQl90bSgaP4gojrlyMsMa2mLwNyh_Kp9DM-WMD7e/s1600/matrix+dream.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhqh_YcxEyeE0jVNeKvdUy9lZIzAL8KCTWjS9KzUHvF0P58-bUD0EUgvviY59p1jecwe3Ktig1fANWZjh704LyOD4n2GN7SibIwdD7eNQl90bSgaP4gojrlyMsMa2mLwNyh_Kp9DM-WMD7e/s200/matrix+dream.jpg" width="197" /></a>To boost the 'dream resolution' of any viable decoding machine, the engineer would need to scan participants for much MUCH longer, using many more visual exemplars to build up an enormous database of brain scans to use as a reference for interpreting more subtle dream patterns. In this study, the researchers took advantage of prior knowledge of specific dream content to limit their database to a manageable size. By verbally assessing the content of dreams first, they were able to focus on just a relatively small subset of all the possible dream content one could imagine. If you wanted to build an all-purpose dream decoder, you would need an effectively infinite database, unless you could discover a clever way to generalise from a finite set of exemplars to reconstruct infinitely novel content. This is an exciting area of active research (e.g., see <a href="http://gallantlab.org/publications/2012.Huth.etal.pdf" target="_blank">here</a>).<br />
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Another major barrier to a commercially available model is that you would also need to characterise this data for each individual person. Everyone's brain is different, unique at birth and further shaped by individual experiences. There is no reason to believe that we could build a reliable machine to read dreams without taking this kind of individual variability into account. Each dream machine would have to be tuned to each person's brain.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgjCyQdrb-8KJST3lLVdKPquvZnu2Xu7F1GMfLELZXN0-CP_8zTNC0F_s8BzGg6JNBAQ9_Feyxf3kOYlY-3P2JfriCkHig9PUu62cJoOHm1-djQDaX-OE4TNvYy6UIVn2chdcd9qBfndqXL/s1600/dreamJournal.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><br />
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Finally, it is also worth noting that the method that was used in this experiment requires some pretty expensive and unwieldy machinery. Even if all the challenges set out above were solved, it is unlikely that dream readers for the home will be hitting the shelves any time soon. Other cheaper, and more portable methods for measuring brain activity, such as EEG, can only really be used to identify difference sleep stages, not what goes on inside them. Electrodes placed directly into the brain could be more effective, but at the cost of invasive brain surgery.<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgjCyQdrb-8KJST3lLVdKPquvZnu2Xu7F1GMfLELZXN0-CP_8zTNC0F_s8BzGg6JNBAQ9_Feyxf3kOYlY-3P2JfriCkHig9PUu62cJoOHm1-djQDaX-OE4TNvYy6UIVn2chdcd9qBfndqXL/s1600/dreamJournal.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="166" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgjCyQdrb-8KJST3lLVdKPquvZnu2Xu7F1GMfLELZXN0-CP_8zTNC0F_s8BzGg6JNBAQ9_Feyxf3kOYlY-3P2JfriCkHig9PUu62cJoOHm1-djQDaX-OE4TNvYy6UIVn2chdcd9qBfndqXL/s200/dreamJournal.jpg" width="200" /></a><br />
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For the moment, it is probably better just to keep a dream journal.<br />
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Reference:<br />
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Horikawa, Tamaki, Miyawaki & Kamitani (2013) Neural Decoding of Visual Imagery During Sleep, Science [<a href="http://www.sciencemag.org/content/early/2013/04/03/science.1234330.abstract" target="_blank">here</a>]<br />
<br />StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com4tag:blogger.com,1999:blog-8524853819129983649.post-77742345270852848902013-04-16T03:32:00.000-07:002013-04-16T08:37:18.769-07:00Statistical power is truth power<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhElHwiqIBnKx-vxpa42_0rADXotRcxmWUorSaXWa5r7nt7T5vP0b4P4kfOqw_ck1-RKnbVcTYAb_694sAUyvoKkrjspdNsSFE9YUfkrYUxnlFFxjCebS2tXsK92UfKtvSVSV5z2KPbuBnQ/s1600/StatPower.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhElHwiqIBnKx-vxpa42_0rADXotRcxmWUorSaXWa5r7nt7T5vP0b4P4kfOqw_ck1-RKnbVcTYAb_694sAUyvoKkrjspdNsSFE9YUfkrYUxnlFFxjCebS2tXsK92UfKtvSVSV5z2KPbuBnQ/s320/StatPower.png" width="208" /></a></div>
This week, <a href="http://www.nature.com/nrn/journal/vaop/ncurrent/full/nrn3475.html" target="_blank">Nature Reviews Neuroscience</a> published an important article by Kate Button and colleagues quantifying the extent to which experiments in neuroscience may be statistically underpowered. For a number of excellent, and accessible summaries of the research, see <a href="http://phenomena.nationalgeographic.com/2013/04/10/neuroscience-cannae-do-it-capn-it-doesnt-have-the-power/" target="_blank">here</a>, <a href="http://bps-research-digest.blogspot.co.uk/2013/04/serious-power-failure-threatens-entire.html" target="_blank">here</a>, <a href="http://j0ns1m0ns.blogspot.co.uk/2013/04/appropriately-powered-replications-what.html" target="_blank">here</a> and this one in the <a href="http://www.guardian.co.uk/science/sifting-the-evidence/2013/apr/10/unreliable-neuroscience-power-matters?CMP=twt_gu" target="_blank">Guardian </a>from the lead author of the research.<br />
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The basic message is clear - collect more data! Data collection is expensive, and time consuming, but underpowered experiments are a waste of both time and money. Noisy data will decrease the likelihood detecting important effects (false negative), which is obviously disappointing for all concerned. But noisy datasets are also more likely to be over-interpreted, as the disheartened experimenter attempts to find something interesting to report. With enough time, and effort, trying lots of different <a href="http://blogs.discovermagazine.com/neuroskeptic/2012/10/14/more-on-false-positive-neuroimaging/#.UWhDx5Okras" target="_blank">analyses</a>, something 'worth reporting' will inevitably emerge, even by chance (false positive). Put a thousand monkeys to a thousand typewriters, or leave an enthusiastic researcher alone long enough with a noisy data set, and eventually something that reads like a coherent story will emerge. If you are really lucky (and/or determined), it might even sound like a pretty good story, and end up published in a high-impact journal.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiZDfQ1l6C9PuowIEFDvWqzrJW_F3o5mOO95EKLhQ0gtRk8pNtfmSnoGx_K4lXPs4Uq88T7BbJIGEfit5EIuK85MIdwPteIQAz-69A2O7Gb19S2Y8m0uoTIsjp1uhaeGr8_84kV0Io2w7TZ/s1600/bogeyman.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="163" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiZDfQ1l6C9PuowIEFDvWqzrJW_F3o5mOO95EKLhQ0gtRk8pNtfmSnoGx_K4lXPs4Uq88T7BbJIGEfit5EIuK85MIdwPteIQAz-69A2O7Gb19S2Y8m0uoTIsjp1uhaeGr8_84kV0Io2w7TZ/s200/bogeyman.jpg" width="200" /></a>This is the classic Type 1 error, the bogeyman of undergraduate Statistics 101. But the problem of false positives is very real, and continues to plague empirical research, from biological <a href="http://www.nature.com/nature/journal/v483/n7391/full/483531a.html" target="_blank">oncology</a> to social <a href="http://www.nature.com/news/replication-studies-bad-copy-1.10634" target="_blank">psychology</a>. Failure to replicate published results is the diagnostic marker of a systematic failure to separate signal from noise.<br />
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There are many bad scientific practices that increase the likelihood of false positives entering the literature, such as <a href="http://doingbayesiandataanalysis.blogspot.co.uk/2012/07/sampling-distributions-of-t-when.html" target="_blank">peeking</a>, <a href="http://www.ncbi.nlm.nih.gov/pubmed/22006061" target="_blank">parameter tweaking</a>, and <a href="http://www.badscience.net/2011/08/brain-imaging-studies-report-more-positive-findings-than-their-numbers-can-support-this-is-fishy/" target="_blank">publication bias</a>, and there are some excellent initiatives out there to clean up these common forms of bad research practice. For example, Cortex has introduced a <a href="http://neurochambers.blogspot.co.uk/2013/04/scientific-publishing-as-it-was-meant_10.html" target="_blank">Registered Report</a> format that should bring some rigour back to hypothesis testing, <a href="http://www.psychologicalscience.org/index.php/news/releases/taking-on-the-challenges-of-replication-in-psychological-science.html" target="_blank">Psychological Science</a> in now hoping to encourage replications and Nature Neuroscience has drawn up clearer <a href="http://www.nature.com/neuro/journal/v16/n1/full/nn0113-1.html" target="_blank">guidelines</a> to improve statistical practices.<br />
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These are all excellent initiatives, but I think we also need to consider simply increasing the margin of error. In a previous post, I argued that the accepted statistical threshold is far too lax. A <a href="http://the-brain-box.blogspot.co.uk/2012/09/must-we-really-accept-1-in-20-false.html" target="_blank">1-in-20 false</a> discovery rate already seems absurdly permissive, but if we consider in all the other factors that invalidate basic statistical assumptions, then the true rate of false positives must be extremely high (perhaps '<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0020124" target="_blank">Why Most Published Research Findings are False</a>'). To increase the safety margin seems like an obvious first step to improving the reliability of published findings.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjItjt6b15g9ceg1WhAHxB0Q461TLXjXk53PAnuFGPJP7PRlOYB9EZYOnMbKZnMEketEzUpKPGbk241AO9srRPcqRSWg18B-I6-1Wi-kIsqY5lbC1YoAVuNx45jgwmMjjUYKGkwUBV4nCFG/s1600/big+data.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="133" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjItjt6b15g9ceg1WhAHxB0Q461TLXjXk53PAnuFGPJP7PRlOYB9EZYOnMbKZnMEketEzUpKPGbk241AO9srRPcqRSWg18B-I6-1Wi-kIsqY5lbC1YoAVuNx45jgwmMjjUYKGkwUBV4nCFG/s200/big+data.jpg" width="200" /></a>The downside, of course, to a more stringent threshold for separating signal from noise is that it demands a lot more data. Obviously, this will reduce the total number of experiments that can be conducted for the same amount of money. But as I recently argue in the <a href="http://www.guardian.co.uk/science/blog/2013/apr/16/folly-science-shoestring" target="_blank">Guardian</a>, science on a shoestring budget can lead to more harm than good. If the research is important enough to fund, then it is even more important that it is funded properly. Spreading resources too thinly will only add noise and confusion to the process, leading further research down expensive and time-consuming blind alleys opened up by false positives.<br />
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So, the take home message is simple - collect more data! But how much more?<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgzQdgpMjVowqlJ4AZzTBXry77R7FGiKGa-pRMYZXn-LLZLhNhMNGha093rcymA-SnKE2ceq0_SG1jED8YVlCyD8-aVxI-vBMvF-1PO_MtmHFtI7t-mhuKke7OY8lNsG3SN3Dkddp6TNG0q/s1600/Power+Analysis.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="150" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgzQdgpMjVowqlJ4AZzTBXry77R7FGiKGa-pRMYZXn-LLZLhNhMNGha093rcymA-SnKE2ceq0_SG1jED8YVlCyD8-aVxI-vBMvF-1PO_MtmHFtI7t-mhuKke7OY8lNsG3SN3Dkddp6TNG0q/s320/Power+Analysis.png" width="320" /></a>Matt Wall recently posted his thoughts on <a href="http://computingforpsychologists.wordpress.com/2013/04/11/comment-on-the-button-et-al-2013-neuroscience-power-failure-article-in-nrn/" target="_blank">power analyses</a>. These are standardised procedures for estimating the probability that you will be able to detect a significant effect, given a certain effect size and variance, for a given number of subjects. This approach is used widely for planning clinical studies, and is essentially the metric that Kate and colleagues use for demonstrate the systematic lack of statistical power in the neuroscience literature. But there's an obvious catch 22, as Matt points out. How are you supposed to know the effect size (and variance) if you haven't done the experiment? Indeed, isn't that exactly why you have proposed to conduct the experiment? To sample the distribution for an estimate of effect size (and variance)? Also, in a typical experiment, you might be interested in a number of possible effects, so which one do you base your power analysis on?<br />
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I tend to think that power analysis is best served for clinical studies, in which there is already a clear idea of the effect size you should be looking for (as it is bounded by practical concerns of clinical relevance). In contrast, basic science is often interested in whether there is an effect, <i>in principle</i>. Even if very small, it could be of major theoretical interest. In this case, there may be no lower bound effect size to impose, so without pre-cognition, it seems difficult to see how to establish the necessary sample size. Power calculations would clearly benefit replication studies, but it difficult to see how they could be applied for planning new experiments. Researchers can make a show of power calculations, by basing effect size estimations on some randomly selected previous study, but this is clearly a pointless exercise.<br />
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Instead, researchers often adopt rules of thumb, but I think the new rule of thumb should be: double your old rule of thumb! If you were previously content with 20 participants for fMRI, then perhaps you should recruit 40. If you have always relied on 100 cells, then perhaps you should collect data from 200 cells instead. Yes, these are essentially still just numbers, but there is nothing arbitrary about improving statistical power. And you can be absolutely sure that the extra time and effort (and cost) will pay dividends in the long run. You will spend less time analysing your data trying to find something interesting to report, and you will be less likely to send some other research down the miserable path of persistent failures to replicate your published false positive.<br />
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<br />StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com3tag:blogger.com,1999:blog-8524853819129983649.post-22180721102099284612013-03-12T13:20:00.002-07:002013-06-28T03:32:26.580-07:00Book review: Hallucinations by Oliver Sacks<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjnjMrny4Qh77mhI2aZkqY2TV6OVzr7O00p-n60AyQ0_osTpjfaF724AJO6MvkS5jEPic6Ssz8OVtA-4YEl-Y8n1s_2qc1KvGfnO_BzV6gOjBS4Lay4hP0lAycytLxKl0iccfbIdcDfdNZj/s1600/Hallucinations_HBR_Cover.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjnjMrny4Qh77mhI2aZkqY2TV6OVzr7O00p-n60AyQ0_osTpjfaF724AJO6MvkS5jEPic6Ssz8OVtA-4YEl-Y8n1s_2qc1KvGfnO_BzV6gOjBS4Lay4hP0lAycytLxKl0iccfbIdcDfdNZj/s200/Hallucinations_HBR_Cover.jpg" width="130" /></a></div>
I read Hallucinations over the Christmas break, and have been meaning to post a book review ever since. Oliver Sacks will be discussing his book tomorrow at Warwick University, where he is currently a <a href="http://www2.warwick.ac.uk/newsandevents/pressreleases/dr_oliver_sacks/" target="_blank">visiting professor</a>. I have booked my seat, and am looking forward to it. I will post my review of his talk, and anything new I learn at the discussion soon.<br />
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**Now posted** At the <a href="http://the-brain-box.blogspot.co.uk/2013/06/book-review-hallucinations-by-oliver.html">Brain Box</a>, and also cross-posted at <a href="http://www.nature.com/scitable/blog/brain-metrics/what_do_hallucinations_tell_us">Brain Metrics</a>, a Scitable Blog hosted by Nature Education.<br />StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-46300647485699804292013-02-24T04:58:00.000-08:002013-02-24T04:58:53.325-08:00Research Briefing: Attention restores forgotten items to visual short-term memory<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj8leqGpG2GUcP2Q32o9F8F9PtxnBIpXH06-dQjzIoaw_Fqb2ugO8Xq08cqyn8VXcWbGuGi4QC1RoPE9eh4s7brNN9kiI8gnqS7dQ7nCo4SN3finCtdwIArRCqYLI6_Xv2guqObJ0q2U7Ib/s1600/ps-cover-for-journals.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj8leqGpG2GUcP2Q32o9F8F9PtxnBIpXH06-dQjzIoaw_Fqb2ugO8Xq08cqyn8VXcWbGuGi4QC1RoPE9eh4s7brNN9kiI8gnqS7dQ7nCo4SN3finCtdwIArRCqYLI6_Xv2guqObJ0q2U7Ib/s200/ps-cover-for-journals.jpg" width="138" /></a></div>
Our paper, just out in Psychological Science, describes the final series of experiments conducted by Alexandra Murray during her PhD with Kia Nobre and myself at the Department of Experimental Psychology, Oxford University. Building on previous research by Kia and others in the <a href="http://www.brainandcognition.org/" target="_blank">Brain and Cognition Lab</a>, these studies were designed to test how selective attention modulates information being held in mind, in a format known as visual short-term memory (VSTM).<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRM7xwvTwTVlfr0IysQ3GAwb2LEV3fOuyK0P8yf5K1m_VsQTboJ1-kqN2rZ-4Pc8leO6RX8ZX7Luvr7h7cgBTn3M38a3oJBP9MBFCQaMxY9qnIyqdPywdxMNuUZ2ZFtGm1zvmr_4Ra-Qiy/s1600/Descartes-reflex.JPG" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRM7xwvTwTVlfr0IysQ3GAwb2LEV3fOuyK0P8yf5K1m_VsQTboJ1-kqN2rZ-4Pc8leO6RX8ZX7Luvr7h7cgBTn3M38a3oJBP9MBFCQaMxY9qnIyqdPywdxMNuUZ2ZFtGm1zvmr_4Ra-Qiy/s200/Descartes-reflex.JPG" width="188" /></a>Typically, VSTM is thought of as a temporary buffer for storing a select subset of information extracted during perceptual processing. This buffer is typically assumed to be insulated from the constant flux of sensory input streaming continuously into the brain, allowing the most important information to be held in mind beyond the duration of sensory stimulation. This way, VSTM enables us to use visual information to achieve longer-term goals, helping to free us from direct stimulus-response contingencies (right).<br />
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Previous studies have shown that attention is important for keeping visual information in mind. For example, <a href="http://psychweb.uoregon.edu/~pk_lab/documents/Awh%20Jonides%20--%20Attention%20and%20working%20memory%20--%20TICS.pdf" target="_blank">Ed Awh</a> and colleagues have suggested that selective attention is crucial for rehearsing spatial information in VSTM, just like inner speech helps us keep a telephone number in mind. Our results described in this paper further suggest that attention is not simply a mechanisms for maintenance, but is also important for converting information into a retrievable format.<br />
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In long term-memory research, retrieval mechanisms are often considered as important to memory performance as the storage format. It is all well and good if the information is stored, but to what end if it cannot be retrieved? We think that retrieval is also important in VSTM - valuable information could be stored in short-term traces that are not directly available for memory retrieval. In this study, we show that attention can be directed to such memory traces to convert them into a format that is easier to use (i.e., retrieve). In this respect, attention can be used to restore information to VSTM for accurate recall.<br />
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We combined behavioural and psychophysical approaches to show that attention, directed to memory items about one second <i>after </i>they had been presented, increases the discrete probability of recall, rather than a more perceptual improvement in the precision of recall judgements (for relevant methods, see also <a href="http://www.sobell.ion.ucl.ac.uk/pbays/pdf/BayHus08.pdf" target="_blank">here</a>). This combination of approaches was necessary to infer a discrete state transition between retrievable and non-retrievable formats.<br />
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Next step? <a href="https://twitter.com/tom_hartley" target="_blank">Tom Hartley</a> asked on twitter: what happened to the unattended items in memory? We did not address this question in this study, and the current literature presents a mixed picture, some suggesting the attention during maintenance impairs memory for unattended items (<a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2150741/" target="_blank">see</a>), whereas others find no such suppression effect (<a href="http://www.ncbi.nlm.nih.gov/pubmed/12536137" target="_blank">see</a>). It is possible that differences in strategy could account for some of the confusion.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhlah7jcsXn_C7mkYY50rpEvpTY34Qb0GSDDSqj4mqEmFcKZxdGTryrS6-2rg9ZVGI6VlXRey_K9odc7LqD26rUwpVEeJ8TzXaVq0tY_lNIV2IV5VMWx_aDTTIQJaFEWTxbpFVvTguayD_I/s1600/14716837-road-sign-showing-contradictory-directions.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhlah7jcsXn_C7mkYY50rpEvpTY34Qb0GSDDSqj4mqEmFcKZxdGTryrS6-2rg9ZVGI6VlXRey_K9odc7LqD26rUwpVEeJ8TzXaVq0tY_lNIV2IV5VMWx_aDTTIQJaFEWTxbpFVvTguayD_I/s1600/14716837-road-sign-showing-contradictory-directions.jpg" /></a>To test the effect on unattended items in behavioural studies, researchers typically probe memory for unattended items every so often. This presents a contradiction to the participant - sometimes uncued items will be relevant for task performance, therefore individuals need to decide on an optimal strategy (i.e., how much attention to allocate to uncued items, just in case...). A cleaner approach is to use brain imaging to measure the neural consequence for unattended items. The principal advantage is that you don't need to confuse your participants with a mixed message: attend to the cued item, even though we might ask you about one of the other ones!! <br />
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<u>References</u>:<br />
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Awh & Jonides (2001) Overlapping mechanisms of attention and spatial working memory. TICS (<a href="http://psychweb.uoregon.edu/~pk_lab/documents/Awh%20Jonides%20--%20Attention%20and%20working%20memory%20--%20TICS.pdf" target="_blank">pdf</a>)<br />
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Bays & Husain (2008) Dynamic shifts of limited working memory resources in human vision. Science (<a href="http://www.sobell.ion.ucl.ac.uk/pbays/pdf/BayHus08.pdf" target="_blank">pdf</a>)<br />
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Landman, Spekreijse, & Lamme (2003). Large capacity storage of integrated objects before change blindness. Vision Research (<a href="http://www.ncbi.nlm.nih.gov/pubmed/12536137" target="_blank">link</a>).<br />
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Matsukura, Luck, & Vecera (2007). Attention effects during visual short-term memory maintenance: Protection or prioritization? Perception & Psychophysics (<a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2150741/#R12" target="_blank">link</a>).<br />
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Murray, Nobre, Clark, Cravo & Stokes (2013) Attention Restores Discrete Items to Visual Short-Term Memory. Psychological Science (<a href="https://docs.google.com/file/d/0Byf6yMJNMU9jTk16Y1pkTnprQnc/edit?usp=sharing" target="_blank">pdf</a>)<br />
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<br />StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-19596873975311066942013-02-23T13:20:00.000-08:002013-02-24T12:00:32.754-08:00Biased Debugging<div class="separator" style="clear: both; text-align: center;">
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhDuifp6IZfW7LPeRepb8TERyKFRwHqCC4nW3yLRPyj969bC2hZ2l26C5sLZ6SweTFDPumAbLWrewrKLAYIGkDVuOs_ep0kvVYZIbw9gr6vHxA5T3XKHRJnKbqUllU3OE9DRn0VW5i32krC/s1600/IEScriptDebuggingError.png" style="clear: left; display: inline !important; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhDuifp6IZfW7LPeRepb8TERyKFRwHqCC4nW3yLRPyj969bC2hZ2l26C5sLZ6SweTFDPumAbLWrewrKLAYIGkDVuOs_ep0kvVYZIbw9gr6vHxA5T3XKHRJnKbqUllU3OE9DRn0VW5i32krC/s200/IEScriptDebuggingError.png" /></a><br />
We all make <a href="http://hadonejob.com/">mistakes</a> - Russ Poldrack's recent blog <a href="http://www.russpoldrack.org/2013/02/anatomy-of-coding-error.html">post</a> is an excellent example of how even the most experienced scientists are liable to miss a malicious bug in complex code. It could be the mental equivalent of missing a single double negative in a 10,000 word essay, or a split-infinite that Microsoft word fails to detect or even a bald-faced typo underlined in red that remains unnoticed by the over-familiar eyes of the author.<br />
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In the case reported by Russ last week, although there was an error in the analysis, the actual result fit their experimental hypothesis and slipped through undetected. It was only when someone else independently analysed the same data, but failed to reproduce the exact result, that alarm bells sounded. Luckily, in this case the error was detected before anything was committed to print, but the warning is clear. Obviously, we need to be more careful, and cross-check our results more carefully.<br />
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Here, I argue that we also need to think a bit more carefully about bias in the debugging process. Almost certainly, it was no coincidence that Russ's undetected error also yielded a result that was consistent with the experimental hypothesis. I argue that the debugging process is inherently biased, and will tend to seek out false positive findings that conform to our prior hopes and expectations. <br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEcvSluUVNo5oI2QiVZwYCvoc-0cfPNHi6LUINFL2s5ir7178TQJA3onpyAn1c8oPL2KhdwpfuI0ydN95jt6vTpPa6O9-7xpXnB2_dlHr_RD7AbWe_b8mXiZ-loEUX0-hA6u4B24nFlruM/s1600/Title-%C2%A0Too+much+noiseArtist-%C2%A0Primoz+Zorko.gif" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjEcvSluUVNo5oI2QiVZwYCvoc-0cfPNHi6LUINFL2s5ir7178TQJA3onpyAn1c8oPL2KhdwpfuI0ydN95jt6vTpPa6O9-7xpXnB2_dlHr_RD7AbWe_b8mXiZ-loEUX0-hA6u4B24nFlruM/s200/Title-%C2%A0Too+much+noiseArtist-%C2%A0Primoz+Zorko.gif" /></a>Data analysis is noisy</h4>
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Writing complex customised analysis routines is crucial in leading-edge scientific research, but is also error prone. Perfect coding is as unrealistic as perfect prose - errors are simply part of the creative process. When composing a manuscript, we may have multiple co-authors to help proofread numerous versions of the paper, and yet even then we often find a few persistent grammatical errors, split infinitives, double negatives slip through the net. Analysis scripts, however, are less often so well scrutinised, line by line, variable by variable.<br />
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If lucky, coding errors just cause our analyses to crash, or throw up a clearly outrageous result. Either way, we will know that we have made a mistake, and roughly where we erred - we can then switch directly to debugging mode. But what if the erroneous result looks sensible? Just by chance, what if the spurious result supports your experimental hypothesis? What are the chances that you will continue to search for errors in your code when the results make perfect sense?<br />
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Your analysis script might contain hundreds of lines of code, and even if you do go through each one, we are notoriously bad at detecting errors in familiar script. Just think of the last time you asked someone else to read draft prose because you had become blind to typos in the text that you have read a million times before. By that stage, you know exactly what the text should say, and that is the only thing you can read any more. Unless you recruit fresh eyes from a willing proofreader, or your attention is directed to specific candidate errors, you will be pretty bad at seeing even blatant mistakes right in front of you. <br />
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Debugging is non-random</h4>
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OK, analysis is noisy - so what? Data are noisy too, isn't it all just part of the messy business of empirical science? Perhaps, but the real problem is that the noise is not random. On the contrary, debugging is systematically biased to favour results that conform to our prior hopes and expectations, that is, our theoretical hypotheses. <br />
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If an error yields a plausible result by chance, it is far less likely to be detected and corrected than if the error throws up a crazy result. Worse, if the result is not even crazy, but just non-significant or otherwise 'uninteresting', then the dejected researcher will presumably spend longer looking for potential mistakes that could 'explain' the 'failed analysis'. In contrast, if the results looks just fine, why rock the boat? This is like a drunkard's walk that veers systematically toward wine bottles to the left, and away from police to the right.<br />
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More degrees of freedom for generating false positives</h4>
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With recent interest in myriad bad practises that boost false positive rates far beyond the assumed statistical probabilities (e.g., see Alok Jha's <a href="http://www.guardian.co.uk/science/2012/sep/13/scientific-research-fraud-bad-practice">piece in the Guardian</a>), I suggest that biased debugging could also contribute to the proliferation of false positives in the literature, especially in the neuroimaging literature. Biased debugging is also perhaps more insidious, because the pull towards false positives is not as obvious in debugging as it is with cherry-picking, data peeking, etc. Moreover, it is perhaps less obvious how to avoid the bias in debugging practices. As Russ notes in his post, code sharing is a good start, but it is not sufficient - errors can remain undetected even in shared code, especially if not widely used. The best possible safeguard is independent reanalysis - to reproduced identical results using independently written analysis scripts. In this respect, it is more important to share the data rather than the analysis scripts, which should not be re-run with blind faith! <br />
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See also: <a href="http://www.russpoldrack.org/2013/02/anatomy-of-coding-error.html">http://www.russpoldrack.org/2013/02/anatomy-of-coding-error.html</a></div>
StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com6tag:blogger.com,1999:blog-8524853819129983649.post-63419618850613924182013-01-17T04:03:00.001-08:002013-01-17T04:03:05.668-08:00Research Briefing: Targeting "silent" brain areas with TMS<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiRuD59kQ4tGEb2a9McHiDqqzEuM7zUcEnRMR-20RWaQfA1fX-aWFfPMdllgSrUPFqJGZSmaXezfbrXEJeOm31SJ4EYrtvKy-RLvYemMlbyIbQp5YhlSna7qmGC24u-rGYsMmrTj3GbencH/s1600/download.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiRuD59kQ4tGEb2a9McHiDqqzEuM7zUcEnRMR-20RWaQfA1fX-aWFfPMdllgSrUPFqJGZSmaXezfbrXEJeOm31SJ4EYrtvKy-RLvYemMlbyIbQp5YhlSna7qmGC24u-rGYsMmrTj3GbencH/s200/download.jpg" width="200" /></a><br />
A major challenge in neuroscience is how to study brain processes that are securely encased within the skull. Over the last twenty years, there has been enormous progress in non-invasive brain imaging methods. In particular, functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) allow researchers to measure brain activity from outside the head.<br />
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Although brain imaging methods allow us to peer inside the head and watch the brain in action, we also need to be able to perturb brain function to understand more fully what observed brain activity is actually doing. We will never understand the brain by just watching it - we also need to be able to poke around to see what happens when certain processes are disrupted. In formal terms, we can only verify causality by disrupting brain activity and observing the consequences.<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEipP7m4zhK5PUpIb4Fc7BDkekN1UJnx5uBemnS93VwZ8pJWRh6Mg8iy2tV3D0YpxtJuWrSK-UjxoQOyCdkzxeoc3ZVq2-0Be5Ht9zkLVtLpyhKqR8zWtQxdfrW2d8Y_bI_3gM1Hot4W_mgB/s1600/TMS.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="179" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEipP7m4zhK5PUpIb4Fc7BDkekN1UJnx5uBemnS93VwZ8pJWRh6Mg8iy2tV3D0YpxtJuWrSK-UjxoQOyCdkzxeoc3ZVq2-0Be5Ht9zkLVtLpyhKqR8zWtQxdfrW2d8Y_bI_3gM1Hot4W_mgB/s200/TMS.jpg" width="200" /></a><br />
The most effective method for non-invasive brain disruption is transcranial magnetic stimulation (TMS). TMS is able to disrupt brain activity by delivering a focal magnetic pulse to the overlying scalp surface. The magnetic field passes through the scalp and skull, stimulating brain cells, thereby disrupting brain function.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhAiH3spF7_RauTNBVWwpYRHG1ghiRJci8zhnGQ70E9csHZZ9q3V_Rj2O_6_7Yh3WYPrMtz7aoMs13U4f38Clp4WFjCB4m6O0hBAz32ESrzGOjfrV8eWc-W9m9bujGchgOcZBKxXjS-AwTj/s1600/neuronav.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhAiH3spF7_RauTNBVWwpYRHG1ghiRJci8zhnGQ70E9csHZZ9q3V_Rj2O_6_7Yh3WYPrMtz7aoMs13U4f38Clp4WFjCB4m6O0hBAz32ESrzGOjfrV8eWc-W9m9bujGchgOcZBKxXjS-AwTj/s1600/neuronav.jpg" /></a></div>
TMS is the only method currently available in human neuroscience to disrupt specific brain areas and measure the consequence on brain function. TMS has been in use in labs across the world for more than 25 years, and sophisticated methods have been developed for targeting specific brain areas (see neuronavigation, pictured right). Nevertheless, it remain relatively unclear exactly how best to set stimulate intensity.<br />
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Setting the right stimulation level is essential for safe and effective use of TMS. Over-stimulation can cause adverse effects, such as seizure. From an experimental point of view, over-stimulation also reduces the focality of disruption, therefore complicating the interpretation of any effects. On the other hand, under-stimulation could compromise treatment in clinical settings, and lead to false negative results in research. Poor control over the stimulation intensity also compromises experimental comparisons between treatment conditions.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgY_D6e3FfVShxLSUCJ1edvHJ5Bt-3QI-WWNxT_qI0WU_J9MIl1xdlOeH8MS1rH1oUDCPRl9xO6IyP71cxNsGn30OkJaoORR02tNJu0_z1ikCcExFHMgr8dSrJuQPQL3AoTZ-G74oFCmdq9/s1600/skull+thick.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="140" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgY_D6e3FfVShxLSUCJ1edvHJ5Bt-3QI-WWNxT_qI0WU_J9MIl1xdlOeH8MS1rH1oUDCPRl9xO6IyP71cxNsGn30OkJaoORR02tNJu0_z1ikCcExFHMgr8dSrJuQPQL3AoTZ-G74oFCmdq9/s200/skull+thick.png" width="200" /></a></div>
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg3S1-NJ43MKWsF7MQzeG4NFF1A9LcwdxJ2hFLj600JBCXqCn0I90y6Ba6IScK5vND4up_GU0_VVKbIy2h81r8SjgPIzQf9C_PZK4iSJ_C0Z1GO02Qitdk_d6esROY-IlcD5-EttxQgUMex/s1600/motormap.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg3S1-NJ43MKWsF7MQzeG4NFF1A9LcwdxJ2hFLj600JBCXqCn0I90y6Ba6IScK5vND4up_GU0_VVKbIy2h81r8SjgPIzQf9C_PZK4iSJ_C0Z1GO02Qitdk_d6esROY-IlcD5-EttxQgUMex/s200/motormap.jpg" width="176" /></a><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmmSriadvTMkYeq_yVSgpNR_Dr5VnuRXo-7-7R4OaievTUhyCKcUHGQJEs67gAR72vGRDY1DZtARpFAnmNOWE9-G49mLqZUCqmliH8Ej37zy0XdmsxBGPHTG21fWhlMDVfQqq_SJkr4rtU/s1600/distance+effect.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="171" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmmSriadvTMkYeq_yVSgpNR_Dr5VnuRXo-7-7R4OaievTUhyCKcUHGQJEs67gAR72vGRDY1DZtARpFAnmNOWE9-G49mLqZUCqmliH8Ej37zy0XdmsxBGPHTG21fWhlMDVfQqq_SJkr4rtU/s200/distance+effect.png" width="200" /></a>In a series of methodological studies performed with <a href="http://psych.cf.ac.uk/contactsandpeople/researchfellows/chambers.html" target="_blank">Chris Chambers</a> and others, we previously explored the effect of skull thickness on brain stimulation. It is well known that the flux density of a magnetic field declines as a function of distance. As a direct consequence, if people have thicker skulls, they will require a higher intensity field at the scalp surface to activate underlying brain areas. To quantify this dependency, we varied TMS distance over motor cortex.<br />
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When TMS is applied to primary motor cortex, stimulation triggers a twitch in the muscle associated with the stimulated portion of the motor map (pictured left). This an extremely reliable and repeatable effect, and therefore provides a very useful tool for assessing the effect of TMS. We simply varied distance between the stimulation coil and the target brain region to characterise the relationship between distance and TMS effect (pictured right). From these initial studies, we suggested that TMS protocols could be usefully calibrated at motor cortex, and corrected for distance to derive a distance-independent estimate of cortical excitability. Distance-corrected levels could then be used to determine the appropriate stimulation intensity for 'silent' brain areas, such as non-motor brain areas for which there is no simple index of effective stimulation.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiWeAj7Ds9papCKcY48oOMnPV2ngzPFGdpUqHuD7-4W7hUYNOE1KsR8kBUgzj7EiOpjDGg1VynNV8xyTa-jIM1c-5DR-lXZGQmPWOuPO58bwa6vfKSOqjaBVIUmgVp-AfFUPNCPlOriKPki/s1600/PT+MT.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="172" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiWeAj7Ds9papCKcY48oOMnPV2ngzPFGdpUqHuD7-4W7hUYNOE1KsR8kBUgzj7EiOpjDGg1VynNV8xyTa-jIM1c-5DR-lXZGQmPWOuPO58bwa6vfKSOqjaBVIUmgVp-AfFUPNCPlOriKPki/s200/PT+MT.png" width="200" /></a>However, distance adjusted TMS still relies on the assumption that individual differences in response to TMS are due to variations in a general factor of cortical excitability. In this new study we tested this key assumption. We compared peoples' sensitivity to stimulation of motor cortex with stimulation of their visual cortex (indexed by a visual percept known as a phosphene). We found a systematic relationship between individual differences in sensitivity across stimulation sites, consistent with the idea that a common factor of cortical excitability might account for individual differences in the response to TMS.<br />
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In conclusion, this research suggests that TMS intensity can be calibrated to distance adjusted motor threshold, and applied to other brain areas. For further information, please see our paper <a href="https://dl.dropbox.com/u/15691907/J%20Neurophysiol-2013-Stokes-437-44.pdf">here</a>, or <a href="http://www.ohba.ox.ac.uk/team/Core%20Staff/mark-stokes" target="_blank">contact me</a> directly.<br />
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References<br />
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Stokes, Barker, Dervinis, Verbruggen, Maizey, Adams & Chambers (2013) Biophysical Determinants of Transcranial Magnetic Stimulation: Effects of Excitability and Depth of Targeted Area. Journal of Neurophysiology, 109: 437– 444 [<a href="https://dl.dropbox.com/u/15691907/J%20Neurophysiol-2013-Stokes-437-44.pdf">pdf</a>]<br />
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Stokes, Chambers, Gould, English, McNaught, McDonald & Mattingley (2007) Distance-adjusted motor threshold for transcranial magnetic stimulation. Clinical Neurophysiology, 118(7): 1617-1625 [<a href="http://psych.cf.ac.uk/home2/chambers/Stokes_2007_ClinNeuro.pdf">pdf</a>] <br />
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Stokes, Chambers, Gould, Henderson, Janko, Allen & Mattingley (2005) A simple metric for scaling motor threshold based on scalp-cortex distance: application to studies using transcranial magnetic stimulation. Journal of Neurophysiology, 94(6): 4520-4527 [<a href="http://psych.cf.ac.uk/home2/chambers/Stokes_2005_JNeuro.pdf">pdf</a>]<br />
<br />StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-77658713721387719812013-01-05T11:09:00.001-08:002013-01-05T11:09:44.194-08:00Helium and Neuroscience<div class="separator" style="clear: both; text-align: center;">
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Modern cognitive neuroscience critically depends on helium. The most advanced methods for non-invasive brain imaging, function magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), operate at near absolute zero (~4° Kelvin). This operating temperature can only be maintained with liquid helium. Although helium is the second most abundant element in the universe, helium supplies are strictly limited on Earth.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJsVWARG00YcYmdJss6XP58z2Ejq8tUjZNcbAQU0ioNmT_yjTGC27Nbk_PXWkwrDXCU8630tvrU1j8PjJxIiAeTeh4AsaqPlAd8PZD73dkevFWKCXaWNYPATU4NBWgCR6Pi1u5zB3yytG4/s1600/MEG.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="137" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJsVWARG00YcYmdJss6XP58z2Ejq8tUjZNcbAQU0ioNmT_yjTGC27Nbk_PXWkwrDXCU8630tvrU1j8PjJxIiAeTeh4AsaqPlAd8PZD73dkevFWKCXaWNYPATU4NBWgCR6Pi1u5zB3yytG4/s200/MEG.jpg" width="200" /></a>Recently, global helium shortages have forced many MEG centres into temporary shut down. MRI facilities have so far been less affected, because they require less frequent helium re-fills. But if the situation was to get much worse, then even MRI centres will be forced to shut down. Cooling down the magnet at the heart of MRI can cause major structural damage, potentially requiring a complete refit.<br />
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Writing for the The Independent, science editor <a href="http://www.independent.co.uk/biography/steve-connor" target="_blank">Steven Conner</a> explains some of the key factors at play [<a href="http://www.independent.co.uk/news/science/a-ballooning-problem-the-great-helium-shortage-8439108.html" target="_blank">here</a>]. In an accompanying piece, I provide some more specific details of how recent shortages have affected our research at the Oxford Centre for Human Brain Activity [<a href="http://www.independent.co.uk/news/science/our-research-is-on-ice-due-to-shortage-of-helium-8439110.html" target="_blank">here</a>]. It is impossible to predict how neuroscience methods will have advanced by the time the world's supply has been depleted in the next <a href="http://www.independent.co.uk/news/science/why-the-world-is-running-out-of-helium-2059357.html" target="_blank">30 years or so</a>, but let's hope we have found new methods for non-invasive brain imaging that don't depend on an unavailable element.<br />
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References:<br />
The Independent: <a href="http://www.independent.co.uk/news/science/a-ballooning-problem-the-great-helium-shortage-8439108.html" target="_blank">A ballooning problem: the great helium shortage</a><br />The Independent: <a href="http://www.independent.co.uk/news/science/our-research-is-on-ice-due-to-shortage-of-helium-8439110.html">Our research is on ice due to shortage of helium</a><br />StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-71822566021377437022012-12-15T21:23:00.000-08:002012-12-16T01:15:43.643-08:00Actors volunteer to be hypontised on TV<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjzgnwRIaAqnqa059kla6OoFs-IDGdhzxr-aNv0UEGdj2Oo0N_wKdCh7i87_SBjKlFNtcgaG0AwShQ4o8Nx-hmhSqT9g1q0J1_ZsGZubFtqyvBt2AjlyonnT0XRMOygGgbvIq8YND4y0rhc/s1600/tom+silver.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjzgnwRIaAqnqa059kla6OoFs-IDGdhzxr-aNv0UEGdj2Oo0N_wKdCh7i87_SBjKlFNtcgaG0AwShQ4o8Nx-hmhSqT9g1q0J1_ZsGZubFtqyvBt2AjlyonnT0XRMOygGgbvIq8YND4y0rhc/s1600/tom+silver.jpg" /></a></div>
We are all pretty familiar with the basic formula of <a href="http://en.wikipedia.org/wiki/Stage_hypnosis" target="_blank">stage hypnosis</a>. Supposedly normal people are hypnotised to do silly and embarrassing things before a wide-eyed audience. It is pretty hard to see exactly how the stage hypnotist is able to make normal folks cluck like a chicken (<a href="http://en.wikipedia.org/wiki/Stage_hypnosis" target="_blank">wiki</a> ideas: peer pressure, social compliance, participant selection, ordinary suggestibility, and some amount of physical manipulation, stagecraft, and trickery). But whatever is going on in people's mind, it seems unlikely that these showman have somehow discovered how to master full mind control with the snap of the fingers.<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguSB3Nsv90JsxI1k57uA4jnyrMi_3wNs4ms-jhKfkHL1JDbcts-piLnpa8m3laHDpyv2uTPbliJWHyQwj0tBh8o7dFPBHvgyW-dkLk0I43hG8N79GamWuQG_B9ErjIOOBnudocUSq9PnME/s1600/mind+control.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguSB3Nsv90JsxI1k57uA4jnyrMi_3wNs4ms-jhKfkHL1JDbcts-piLnpa8m3laHDpyv2uTPbliJWHyQwj0tBh8o7dFPBHvgyW-dkLk0I43hG8N79GamWuQG_B9ErjIOOBnudocUSq9PnME/s1600/mind+control.jpg" /></a><br />
Yet still, the idea that hypnosis could be used to control all types of behaviour seems to capture the imagination. Recent TV programmes have dived straight into the sensationalist deep end, to pose the question: can people be hypnotised to commit a cold-blooded assassination? On Channel 4's <a href="http://www.channel4.com/programmes/derren-brown-the-experiments/4od#3258648" target="_blank"><i>Experiment </i>series, Derren Brown</a> claims to show that a normal everyday kind of guy can be plucked off the street and hypnotised to shoot a celebrity at a public gathering. Similarly, hypnotist Tom Silver was employed by the <a href="http://dsc.discovery.com/tv-shows/curiosity/season-2-episodes3.htm" target="_blank">Discovery Channel programme <i>Brainwashed</i> </a>to test the same idea: can Joe Citizen be hypnotised to commit cold blooded murder?<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjf5dwSn2OvGvYa6ug3xONfUPNWdC1bB0ZZ5u4-TGsa8ly6Pr20eTfwqLJTPa7Nx5rW2nT0mi5QqinKiiwwuAKPcOc4zYuRnUug4K8TjDWTxkCaaDj59uEBJNoWS2bbbD9NHZUvmEY7t3yY/s1600/Patty+Hearst.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjf5dwSn2OvGvYa6ug3xONfUPNWdC1bB0ZZ5u4-TGsa8ly6Pr20eTfwqLJTPa7Nx5rW2nT0mi5QqinKiiwwuAKPcOc4zYuRnUug4K8TjDWTxkCaaDj59uEBJNoWS2bbbD9NHZUvmEY7t3yY/s200/Patty+Hearst.jpg" width="134" /></a></div>
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEibicQPuUeXYgHXqVEnr_p02kzCzRKvUtmcV4BaIkE9ZZJIZwaD2xK-SR5gwqIM8h1pzvfW7YkHbA7nFleG0e0HkyxEMgqV19B2fifsOfXpdTxFLE-XfVP6v0n_YQksZ47zoJpBQidgyvcp/s1600/Sirhan+Sirhan.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEibicQPuUeXYgHXqVEnr_p02kzCzRKvUtmcV4BaIkE9ZZJIZwaD2xK-SR5gwqIM8h1pzvfW7YkHbA7nFleG0e0HkyxEMgqV19B2fifsOfXpdTxFLE-XfVP6v0n_YQksZ47zoJpBQidgyvcp/s1600/Sirhan+Sirhan.jpg" /></a><br />
Both shows set up the question by invoking the case surrounding Bobby Kennedy's assassination. In what seems an absurdly flimsy defence, <a href="http://en.wikipedia.org/wiki/Sirhan_Sirhan" target="_blank">Sirhan Sirhan</a> claimed to be hypnotised by secret agents to carry out the killing. The counter-evidence for premeditation was over-whelming, and the appeal was unsuccessful - this is hardly a strong starting point for establishing precedent. Rather, Sirhan Sirhan's defence seems to fit somewhere between desperate appeal and paranoid delusion. The Discovery Channel additionally invoked the case of <a href="http://en.wikipedia.org/wiki/Patty_Hearst" target="_blank">Patty Hearst</a>. This is a fascinating story in its own right. Heiress to the fortune of media mogul William Randolph Hurst (immortalised as Charles Foster Kane by Orson Welles in the classic <a href="http://en.wikipedia.org/wiki/Citizen_Kane" target="_blank">Citizen Kane</a>) was kidnapped by a <a href="http://en.wikipedia.org/wiki/Symbionese_Liberation_Army">self-styled left-wing revolutionary group, involved in bank robberies, two murders, and other acts of violence.</a> After a failed ransom bid, she became an active member of this vanguard army until she was eventually captured by police and put to trial for armed robbery. Her defence, heavily influenced by her extremely influential parents, argued that Patty Hearst had been brainwashed to join the revolutionary group. It seems likely that her parents were unable to accept the more shocking possibility that their daughter would willingly turn on their way of life to adopt an outlaw revolutionary life. Here, the term <i>brainwashed</i> sounds more like an expression of parental disbelief than a systematic process of coercive mind control.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjAY8lrvbt9SA6enGpmY69T9U7lexiiUQZ_zxM4SEDAtal9-3l_m7zb2mp9-QNWUSCktBJAEF5FTTCKBYO48cr9xqoG6P2OcWw6JPViUFHynhUdyHXbVjTN9dnWqTlxat1RMJ0UdOH-Gnmo/s1600/stokes+brainwashed.JPG" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="212" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjAY8lrvbt9SA6enGpmY69T9U7lexiiUQZ_zxM4SEDAtal9-3l_m7zb2mp9-QNWUSCktBJAEF5FTTCKBYO48cr9xqoG6P2OcWw6JPViUFHynhUdyHXbVjTN9dnWqTlxat1RMJ0UdOH-Gnmo/s320/stokes+brainwashed.JPG" width="320" /></a></div>
Despite the weak starting premise for mind control, both shows nevertheless set out to demonstrate that hypnosis can be used to programme an ordinary person to carry out a (mock) assassination. On <i>Brainwashed</i>,<i> </i>I was called in to join a panel of experts to assess a series of 'experiments', starting from relatively benign tests of hypnotic suggestion and culminating in the mock assassination. Our role as the scientific experts was relatively limited, but we were able to observe the overall process reasonably closely. We saw no obvious jiggery pokery during production, although post-production clearly used the usual kinds of selective editing tricks that can mould impressions without explicit falsehoods.<br />
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Most viewers are pretty wise to the fact that the final cut includes only footage that the director wants you to see. Very many hours of footage never make it to screen, leaving plenty of wriggle room to create a 'coherent narrative'. But what did seem to surprise many viewers was the fact that the star 'assassin' of the show turned out to be a <a href="http://uk.imdb.com/name/nm4961273/" target="_blank">part-time actor</a>, not a regular member of the public as the overall narrative implied. A similar minor <a href="http://www.independent.co.uk/arts-entertainment/tv/news/derren-brown-slams-hurtful-accusations-that-zombie-apocalypse-victim-was-fake-8268993.html" target="_blank">scandal</a> erupted when it was suggested that one of Derren Brown's hypnosis subjects was in the acting profession.<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjScqApM8ZIX_x_eaGtpJwT0KCi0rM9_MFc3AV_aEWolb-q96OddURH2vTBCWyUxfKkMdQSvdLpkBc1XE1ohnEKzHxisUe2zqKcmSqH43Rd9huRD25-zEmmB8hh-7zb8Hb4uhHAqGvO6Azu/s1600/IvanTheTerrible.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjScqApM8ZIX_x_eaGtpJwT0KCi0rM9_MFc3AV_aEWolb-q96OddURH2vTBCWyUxfKkMdQSvdLpkBc1XE1ohnEKzHxisUe2zqKcmSqH43Rd9huRD25-zEmmB8hh-7zb8Hb4uhHAqGvO6Azu/s1600/IvanTheTerrible.jpg" /></a><br />
But is it really so surprising that a volunteer who signs up to be on TV turns out to be an actor? Presumably most people who volunteer for these kinds of things are either actors, or at least aspiring actors. And presumably the directors know this too. Even if they don't explicitly advertise for actors, they are very likely to get actors answering to the call for participation. And conveniently, actors will no doubt act the part for the cameras - so what more could a director want? It is not impossible that actors can also be hypnotised (maybe good acting is a form of hypnosis anyway), but it is important to keep in mind the relevant context: TV studio, with lights, cameras, etc; and actors (or similar) who want to be on TV. This scenario is not the stuff of controlled <a href="http://www.guardian.co.uk/science/blog/2012/dec/13/tv-experiments-bad-science" target="_blank">scientific research</a> - needless to say, such shows should be viewed with a healthy scepticism. It maybe not be necessary quite yet to abandon your pre-existing sense that you are more or less in control of your own actions and behaviour.<br />
<br />StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com2tag:blogger.com,1999:blog-8524853819129983649.post-29520471329687734902012-12-13T12:50:00.000-08:002012-12-15T03:06:56.035-08:00Science, LIVE: A made-for-TV experiment<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgvAeC8nSXMT_6aYjwigRYbVs5ryWx9sitAFoMr5FgTdvwAGjArMZhmLimYSN9TB6q86DY3kUrmrZceyFqjuhyB7STdJIx-9JtRyokjrimT-wymoo5P6vbpRh-HqtQGFrfRrkbWDOs0-qox/s1600/Drugs+Live.jpg" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgvAeC8nSXMT_6aYjwigRYbVs5ryWx9sitAFoMr5FgTdvwAGjArMZhmLimYSN9TB6q86DY3kUrmrZceyFqjuhyB7STdJIx-9JtRyokjrimT-wymoo5P6vbpRh-HqtQGFrfRrkbWDOs0-qox/s320/Drugs+Live.jpg" /></a><br />
[also see my related Guardian <a href="http://www.guardian.co.uk/science/blog/2012/dec/13/tv-experiments-bad-science" target="_blank">post</a>]<br />
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A few months ago, Channel 4 attracted considerable attention for their sensationally titled – “Drugs Live: the Ecstasy Trial”. The subject matter was clearly designed to court controversy, with Prof Nutt at the centre of the storm. The show "<a href="http://www.guardian.co.uk/media/2012/sep/27/drugs-live-ecstasy-trial">hooked almost 2 million</a>" viewers on the first night, and triggered a lively debate around highly charged questions, such as: Do we need to focus more on the medical/chemical nature of particular drugs, and less on the moral/legal status? Is it right to film volunteers taking a Class A drug, even for a medial experiment? Is Channel 4 glorifying illegal drugs, or contributing to rational discussion?<br />
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Much was written and said on either side of this debate (e.g. this conversation between <a href="http://www.guardian.co.uk/commentisfree/2012/sep/21/ecstasy-david-nutt-channel-4">Nutt and Manning</a>). However, as an empirical scientist, I would like to draw attention to another more general issue that was perhaps neglected in the mêlée of moral and ethical arguments. I would like to know why TV science is conducting experiments in the first place?<br />
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The made-for-TV experiment</h4>
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Drugs Live was structured around an ethically approved double-blinded experiment to test the effects of MDMA. Data were collected from 25 participants under the influence of MDMA and a placebo control (sugar pill). Tests included questionnaire and computer-based tasks to measure changes in mood and cognition, as well as the ever-TV-friendly fMRI to measure changes in brain activity. This sounds like a reasonable set of experiments, but according to Prof Nutt, the Medical Research Council (MRC) declined to fund the research because it did not "<a href="http://www.guardian.co.uk/commentisfree/2012/sep/21/ecstasy-david-nutt-channel-4">fit in with the MRC's portfolio of addiction</a>". Instead, Channel 4 agreed to pick up the tab, presumably for more financial motives compared to the MRC's commitment to "improve human health through world-class medical research" (from <a href="http://www.mrc.ac.uk/About/Missionstatement/index.htm">mission statement</a>). Perhaps we should celebrate this innovative collaboration between academia and the private sector. In these times of austerity, perhaps TV-funded research is the future big-society answer to maintain Britain’s place as a leading powerhouse of innovation, science and technology.</div>
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And indeed, if the research is well-conducted, the results could provide valuable insights into the effects of MDMA, of genuine scientific interest with important political/social relevance. After many weeks and months of painstaking data analysis, the results could be submitted to a reputable scientific journal for rigorous <a href="http://www.guardian.co.uk/science/lost-worlds/2012/dec/01/dinosaurs-fossils?CMP=twt_fd" target="_blank">peer-review</a>. If the submitted findings are accepted by the academy as sufficiently trustworthy, then the scientific report would be published for consideration by a wider scientific audience. Journal press-releases might then alert the popular news outlets, who may then report these novel findings to their more general readership. </div>
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This is how scientific findings are normally disseminated to the wider audience. Slowly, but surely, complex data yield their secrets to careful systematic analysis. It is not gripping TV, but this systematic process is the foundation of modern scientific research. Made-for-TV science, on the other hand, can by-pass the process completely and stream their own results directly into living rooms across the country. </div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjZoLb55SBPjJHyuSaNY_buLpwS-tRZbnbIpwQBR_pYMxVwIBqFTgfr6b-R55lLTRkI2MxCfrUM5lHUxT0BYCazjwKsiPw7lwo4Ihy4KGP_hWk_jqRNpybDZ_hD0MgX3_uXcs7iAIZ3r8cM/s1600/Drugs+Live+Brain.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjZoLb55SBPjJHyuSaNY_buLpwS-tRZbnbIpwQBR_pYMxVwIBqFTgfr6b-R55lLTRkI2MxCfrUM5lHUxT0BYCazjwKsiPw7lwo4Ihy4KGP_hWk_jqRNpybDZ_hD0MgX3_uXcs7iAIZ3r8cM/s1600/Drugs+Live+Brain.jpg" /></a><br />
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Science to a production schedule: lights, camera, action!</h4>
Maybe science needs a bit more of a can-do attitude. Like Jon Snow, who promises on Drugs Live that “tonight we will get to the bottom of it”. Not in another month, six months, or couple of years - but this very evening! And true to his word, by the end of the first episode Snow can already announce that we have all witnessed “two scientific breakthroughs”. He was not very specific, but we may assume that he was referring to the two brain scans that were rotated on a large plasma screen.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3tsmKNuMcAtD6ODh1dv332bbuWm87pCdkQk5xLkYoW08m710klkX2BlQI-2mnUNY384IhxqBi_0qbEmSl7VlTqnJLsGpEQuAzUPlK_IXsyktRz5eVqtWwMZtmiiwfDgXjU8srORdzq97W/s1600/Drugs+Live+4.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3tsmKNuMcAtD6ODh1dv332bbuWm87pCdkQk5xLkYoW08m710klkX2BlQI-2mnUNY384IhxqBi_0qbEmSl7VlTqnJLsGpEQuAzUPlK_IXsyktRz5eVqtWwMZtmiiwfDgXjU8srORdzq97W/s1600/Drugs+Live+4.jpg" /></a><br />
The first scan, from actor Keith Allen, appeared to show a relative decrease in communication between two areas previously associated with the so-called <a href="http://en.wikipedia.org/wiki/Default_network" target="_blank">default mode network</a>. Firstly, I should leave aside any academic debate about the true nature of this brain network, as tempting as it is to question Prof Nutt’s proclamation that this area is no less than “you, your personality, sense of self”. My purpose here is just to make the point that data from one brain in one volunteer, hastily analyzed and not peer-reviewed, does not constitute a “scientific breakthrough”. Perhaps an interesting hint. A potential clue, maybe. Promising lead, why not? But certainly not a: “scientific breakthrough”.<br />
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The second scan was even more ambiguous. The rotating image appeared to show a number of brain regions we are told were more active when the volunteer closed her eyes after taking MDMA. We are told that this reflects the heightened perceptual experience caused by the drug. But to be blunt, these data look like a mess, like random variation in the MRI signal. This is not really all that surprising, considering it is only one scan from one person, analyzed under the unrealistic time pressure of the TV production schedule. I would be amazed, or even suspicious if the result was any clearer. </div>
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Of course science needs to be simplified for a TV audience. <a href="http://www.pc.rhul.ac.uk/staff/m.wall/" target="_blank">Matt Wall</a>, neuroscientist involved in the Drugs Live programme, says of his experience:<br />
<blockquote class="tr_bq">
"TV needs everything to be black-and-white, and unambiguous... They don’t care that you haven’t run the necessary control experiments, or that the study was only correlational and therefore can’t be used to imply direct causation – they want a neat, clear story above all else"</blockquote>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjvZ8s9z0GZfPD1OOyft1J5KBTiBDqifLvr5T2tsaC1sbd8svWsPsf3ZNCVv9p3yX2Ax5piYKwbBhHFzNHbd3qWbSlzBoaJPJcEK00f5qaCoOIh4WIiE4wRSPH7umVwxN2qFGptakm7zC9F/s1600/science+for+kids.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="152" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjvZ8s9z0GZfPD1OOyft1J5KBTiBDqifLvr5T2tsaC1sbd8svWsPsf3ZNCVv9p3yX2Ax5piYKwbBhHFzNHbd3qWbSlzBoaJPJcEK00f5qaCoOIh4WIiE4wRSPH7umVwxN2qFGptakm7zC9F/s320/science+for+kids.jpg" width="320" /></a></div>
Oversimplifying the deeper complexities "can very often lead to distortions, or ‘<a href="http://en.wikipedia.org/wiki/Lie-to-children" target="_blank">Lies-to-children</a>’". This is a perennial issue for TV science, whether following the classic science reporting formula or made-for-TV experiments. To be able to articulate complex theoretical concepts and technical details to general audience without misrepresentation is a great but rare skill.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgy10ITdp-FyR69V3KHSx_ATAOOcmbMZQ4lWhhfbI99SbSmOd-_AEvkmNLdW1qdHL52yoAK1uNqpzm8bpY2sJoohzl5tk3fhyphenhyphenXKOQz1N7AhWUAp5nVIjVuKBjBlusvrcl9T6mz4MxDW_MTy/s1600/blaine.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgy10ITdp-FyR69V3KHSx_ATAOOcmbMZQ4lWhhfbI99SbSmOd-_AEvkmNLdW1qdHL52yoAK1uNqpzm8bpY2sJoohzl5tk3fhyphenhyphenXKOQz1N7AhWUAp5nVIjVuKBjBlusvrcl9T6mz4MxDW_MTy/s1600/blaine.jpg" /></a></div>
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Astonishing Science</h4>
Jon Snow promised his live audience “astonishing science”. Quite right, TV should bring astonishing science to the wider audience. But this mission is seriously compromised by the production demands associated with made-for-TV experiments.<br />
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Advising the Discovery Channel on a recent made-for-TV experiment, I was told by a production assistant: “you don’t have to always be so cynical, you know.” And I absolutely agree with the sentiment. Science TV should convey the excitement of science, not just the limitations. Just like this production assistant, I am also frustrated by too many caveats. The reason I came to science was to discover something about the world, not just to point out flaws in putative findings. But like any empirical scientist, I have learned many times over not to get too excited over half-baked results. Only solid reliable results are really exciting.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiNBA6sn9r6eIaYU3KLLZYVXJWdhyphenhyphenBmJxxz7JRblD5o6_bbgelgMRUN38RG7SmP4oVJSkn8ZEAzoFkaHFZlISVx86j-esgySYuWi0PA8FD5umG764GbYkc6ghGi0TfbMheDFVLjr5dtrQ6s/s1600/brainwashed.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiNBA6sn9r6eIaYU3KLLZYVXJWdhyphenhyphenBmJxxz7JRblD5o6_bbgelgMRUN38RG7SmP4oVJSkn8ZEAzoFkaHFZlISVx86j-esgySYuWi0PA8FD5umG764GbYkc6ghGi0TfbMheDFVLjr5dtrQ6s/s1600/brainwashed.jpg" /></a>But TV science does not have to be boring. TV has many tricks up its sleeve, such as dramatic music, frenetically paced scene cuts, angled screen shots in darkened laboratories, and expensive props like MRI. All these can be used to covey the excitement of science, without resorting to made-for-TV experiments.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjVW0SlLu77GHVscXzZCFNU7k0DkVPPqvGnA_LXocyOi_nkR3F1xLA3FkzKy6IDV4xRwKqvrtIJhvjH9XqJceECp9sTzibNTm3k_E_GBdvQb-6Pb1Jl9vDgepv1GtPAlJmPwYSPi88cTHfn/s1600/OHBA-10.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjVW0SlLu77GHVscXzZCFNU7k0DkVPPqvGnA_LXocyOi_nkR3F1xLA3FkzKy6IDV4xRwKqvrtIJhvjH9XqJceECp9sTzibNTm3k_E_GBdvQb-6Pb1Jl9vDgepv1GtPAlJmPwYSPi88cTHfn/s200/OHBA-10.jpg" width="150" /></a>
Reality-science TV</h4>
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The enormous success of reality TV tells us that viewers like to experience the activities on screen through people they can relate to. Extrapolating to science TV, I guess viewers like to feel the science experience through personalities that they can relate to. The personal touch can make it seem more real.</div>
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Recently, I was asked to help conduct another made-for-TV brain imaging experiment with the show's presenter as the experimental subject. To fit the production schedule, we had to analyse complex brain imaging data within a matter of hours. We did manage to produce some very rudimentary results within this science-improbable time frame - we had to! Production costs are clocked by the hour.<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgR4zYst61l-RCYUwkLkE9ZlGF6vqs8tZw12oxNunQUjudkjQjm4VhatCqdAueNCTBR978DL9aU3JfBGyWfbtTY32yybhSYaOnZB8ycRpV9i4mG_-g2_clyeAsWupxQjlRZoiQtWlgQg4Hv/s1600/exciting+science.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgR4zYst61l-RCYUwkLkE9ZlGF6vqs8tZw12oxNunQUjudkjQjm4VhatCqdAueNCTBR978DL9aU3JfBGyWfbtTY32yybhSYaOnZB8ycRpV9i4mG_-g2_clyeAsWupxQjlRZoiQtWlgQg4Hv/s1600/exciting+science.jpg" /></a><br />
Of course the actual result was of limited scientific value, and we were naturally so circumspect about what we said on camera that it is hard to see how this data could have been of any great interest to the audience. It was essentially just a brain on a screen: demonstration science - what it <i>looks</i> like to do science.<br />
There is of course no harm in such demonstrations - eye-catching demonstrations are bread and butter tools for conveying the excitement of science. We don't need to pretend that they are also conducting novel scientific research. It is important enough to help convey the process of science without pretending to add to the content of scientific knowledge.There are plenty of good and proper TV production devices for engaging public interest in science. And we expect a little bit of TV gimmickry, it is show biz after all. But why not be content with reporting on science, rather than making science as well?</div>
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Investigative Journalism</h4>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhb8Z8sIRETwsjHbumT58-Dih6O2xpja4wTq_uIt-ympoCzV4xNhRg4JIedQISJpvtr7GlJnkRcMooVWzVWJWMZz9T7QUo4UnHfr91hqMVDSUnz43lyQFoM51q-FMZpyviixVziMlxcfOFR/s1600/investigative+journalism.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhb8Z8sIRETwsjHbumT58-Dih6O2xpja4wTq_uIt-ympoCzV4xNhRg4JIedQISJpvtr7GlJnkRcMooVWzVWJWMZz9T7QUo4UnHfr91hqMVDSUnz43lyQFoM51q-FMZpyviixVziMlxcfOFR/s200/investigative+journalism.jpg" width="165" /></a>The Drugs Live formula is a hybrid of traditional science programme and the exposé. Borrowing from the rich history of the investigative journalism, TV is not just reporting news, but making news as well. The production company can herald exclusive access to a breaking news story:</div>
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“Now, in a UK television first, two live programmes will follow volunteers as they take MDMA, the pure form of ecstasy, as part of a ground-breaking scientific study” [from the Channel 4 series <a href="http://www.channel4.com/programmes/drugs-live-the-ecstasy-trial/episode-guide/series-1" target="_blank">synopsis</a>]</blockquote>
But investigative journalism is also tricky business. The precise outcome of any investigation is impossible to predict, and therefore hard to plan for. Out of the many possible leads, only a minority will reveal something worth reporting. Producers are presumably familiar with the frustration of stories that lead nowhere, and presumably they are reasonably careful about committing to a production schedule until after the results of the investigation are relatively clear. Jumping to premature conclusions can lead to serious false claims, as dramatically highlighted recently by the <a href="http://www.guardian.co.uk/media/2012/nov/10/newsnight-mcalpine-scoop-rumour" target="_blank">Newsnight debacle </a>that cost the BBC general director his job. In a recent<a href="http://www.guardian.co.uk/commentisfree/2012/nov/12/investigative-journalism-newsnight-crisis" target="_blank"> post-mortem</a> of this botched investigation, David Leigh writes: “t<span style="background-color: white;">o be faithful to the evidence" is essential for successful
investigative journalism. And although journalism may not be "rocket science" [in Leigh's words], investigative journalism also demands a genuine commitment to follow the evidence, wherever it leads. </span><br />
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Conflict of interest</h4>
In the shadow of recent revelations of <a href="http://www.guardian.co.uk/science/2012/sep/13/scientific-research-fraud-bad-practice" target="_blank">fraud and serious malpractice</a> across a range of scientific disciplines, from <a href="http://www.nature.com/news/replication-studies-bad-copy-1.10634" target="_blank">psychology</a> to <a href="http://www.nature.com/news/retraction-record-rocks-community-1.11434" target="_blank">anaesthesiology</a>, many scientists have been asking how to improve the scientific process (e.g., see <a href="http://www.guardian.co.uk/commentisfree/2012/sep/14/solution-scientific-fraud-replication" target="_blank">here</a> and <a href="http://neuroskeptic.blogspot.co.uk/2012/10/the-two-problems-with-science.html" target="_blank">here</a>). Televising the process is unlikely to be the answer. </div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj9VM4C1amuFbmQOZN8ub8QsCQJ7UyBSQKpBHUzAH87m_E1NFMfHI-Y3orucfCRfY44rXk-MfSWoVeUUVXG0sqrib_7bj50ClJZnL7sAhZCiNp5OEc9_zAwc2GNhy1MzSWjx4FHX-kkXbBH/s1600/deal+with+the+devil+3.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj9VM4C1amuFbmQOZN8ub8QsCQJ7UyBSQKpBHUzAH87m_E1NFMfHI-Y3orucfCRfY44rXk-MfSWoVeUUVXG0sqrib_7bj50ClJZnL7sAhZCiNp5OEc9_zAwc2GNhy1MzSWjx4FHX-kkXbBH/s1600/deal+with+the+devil+3.jpg" /></a>Although industry-funding can be a valuable source of revenue, we must always seriously consider potential conflicts of interest. Increasingly, concerns have been raised regarding dubious practices in clinical research funded by pharmaceutical companies. Ben Goldacre’s book, <a href="http://www.guardian.co.uk/business/2012/sep/21/drugs-industry-scandal-ben-goldacre" target="_blank">Bad Pharma</a>, is a must read on this serious public health issue. Conflicts of interest can distort many stages of the experimental process to increase the likelihood of finding a particular result. Clinical research is becoming increasingly alert to these problems, and serious steps are being made to avoid the malevolent influence of funding agencies with vested interests.<br />
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But even without a commercial interested in a particular result, the pressure to "find something" noteworthy can also motivate bad scientific practice. In academic circles, the<a href="http://www.nature.com/news/2010/100112/full/463142a.html" target="_blank"> pressure to publish</a> is typically considered a major driving factor in scientific malpractice and fraud.The bottom line for Channel 4, of course, is to maximise audience numbers to boost the value of their commercial time. This does not automatically rule out potential scientific merit of TV-funded experiments, but it certainly is worth bearing in mind, especially when thinking about how the particular demands of TV could compromise scientific method. As discussed above, these include short-cuts and rushed analyses to fit a tight production schedule, as well as the pressure to find "something in the data" by the end of the shoot, however unreliable it might turn out to be later. The experimental approach in Drugs Live was also apparently compromised by recruiting celebrities (and other TV-friendly personalities) as experimental subjects, tapping into the proven success of the reality-TV format but skewing the sample of experimental subjects. It is also hard to imagine that omnipresent TV cameras did not influence the results of the experiment.<br />
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It will be interesting to follow up on this research to see how the results are received within the academic community. There is no reason not to expect some interesting and important findings, but I wonder if the more detailed and scientifically meaningful results will be heralded with as much fanfare as the actual pill-popping on camera. I fear Channel 4 might be more interested in the controversy surrounding MDMA than the science motivating the research.<br />
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StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com2tag:blogger.com,1999:blog-8524853819129983649.post-24796410840012270552012-11-30T10:40:00.003-08:002012-12-07T04:00:29.791-08:00Bold predictions for good science<br />
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Undergraduates are taught proper scientific method. First, the experimenter makes a prediction, then s/he collects data to test that prediction. Standard statistical methods assume this hypothesis driven approach, most statistical inferences are invalid unless this rigid model is followed.</div>
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But very often it is not. Very often experimenters change their hypotheses (and/or analyses methods) <i>after </i>data collection. Indeed, students conducting their first proper research project are often surprised by this 'real-world' truth: "oh, that is how we really do it!". They learn to treat research malpractice like a cheeky misdemeanour. </div>
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After recent interest in science malpractice, fuelled by revelations of outright <a href="http://www.guardian.co.uk/science/2012/sep/13/scientific-research-fraud-bad-practice" target="_blank">fraud</a>, commentators are starting to treat the problem more seriously, especially in psychology and neuroscience. This month, <a href="http://pps.sagepub.com/content/current" target="_blank">Perspectives on Psychological Science</a> devoted an entire issue to the problem of peer-reviewed results that <a href="http://www.nature.com/news/replication-studies-bad-copy-1.10634" target="_blank">fail to replicate</a>, because they were born of bad scientific practice. </div>
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Arguably, scientific journals share much of the responsibility for allowing bad research practices to flourish. Although there maybe little journals can do to stop outright fraud, they can certainly do a lot to improve research culture more generally. Recently, the journal <a href="http://www.journals.elsevier.com/cortex/" target="_blank">Cortex</a> has announced that it will try to do just that. <a href="http://psych.cf.ac.uk/contactsandpeople/researchfellows/chambers.html" target="_blank">Chris Chambers</a>, associate editor, has outlined a new submission format that will strictly demand that researchers conform to the classic experimental model: predictions before data. With the proposed <i><a href="http://neurochambers.blogspot.co.uk/2012/10/changing-culture-of-scientific.html" target="_blank">Registration Report</a></i>, authors will be required to set out their predictions (and design/analysis details) before they collect the data, thus cutting off the myriad opportunities to capitalise on random vagaries in observed data. And although researchers could still lie and make up data, cleaning up the grey area of more routine bad behaviour could have important knock on effects. As I have argued <a href="http://the-brain-box.blogspot.co.uk/2012/09/must-we-really-accept-1-in-20-false.html" target="_blank">elsewhere</a>, bad scientific practice is presumably a fertile breeding ground for more serious acts of fraud.<br />
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This is a bold new initiative, and if successful, could precipitate a major change in the way science is done. For further details, and some interesting discussion, check out this panel discussion on <a href="http://www.nature.com/spoton/event/spoton-london-2012-fixing-the-fraud-how-do-we-safeguard-science-from-misconduct/" target="_blank">Fixing the Fraud</a> at <a href="http://www.nature.com/spoton/2012/11/spoton-london-2012-online-coverage/" target="_blank">SpotOn London 2012</a> and this article in <a href="http://www.guardian.co.uk/science/blog/2012/dec/07/confronting-sloppiness-pervades-science?CMP=twt_fd" target="_blank">the Guardian</a>.</div>
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<iframe allowfullscreen="allowfullscreen" frameborder="0" height="170" src="http://www.youtube.com/embed/3utUERQx2sc?feature=player_embedded" width="320"></iframe>StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com0tag:blogger.com,1999:blog-8524853819129983649.post-57898725469075136762012-10-03T01:46:00.001-07:002012-10-03T05:52:37.773-07:00Distance CodeAccurate brain stimulation requires precise neuroanatomical information. To activate a specific brain region with transcranial magnetic stimulation (TMS), it is important to know where on the scalp to place the induction coil. Commercial neuronavigation systems have been developed for this purpose. However, it is also important to know the depth of the targeted area, because the effect of TMS critically depends on the distance between the stimulating coil and targeted brain area. <br />
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We have developed a simple TMS Distance Toolbox for calculating the distance between a stimulation site on the scalp surface and underlying cortical surface. The toolbox can be download <a href="https://docs.google.com/open?id=0Byf6yMJNMU9jUkFma1dmQkRlaXc" target="_blank">here</a>, and requires Matlab and <a href="http://www.fil.ion.ucl.ac.uk/spm/software/spm8/" target="_blank">SPM8</a>. I will posted further information soon, including user instructions.StokesBloghttp://www.blogger.com/profile/00890404304081225894noreply@blogger.com