In the News: Death wave

Near-death Experience
(Wiki Commons)

Can neuroscience shed light on one of life's biggest mysteries - death? In a paper just published in PNAS, researchers describe a surge of brain activity just moments before death. This raises the fascinating possibility that they have identified the neural basis for near death experiences.

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 'euthanized' (i.e., killed). The exact moment of death was identified as the last regular heartbeat (clinical death). Electroencephalogram (EEG) 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 Figure 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.

Adapted from Fig 1 of Borjogin et al. (2013)

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.

Heightened awareness just after death  

Adapted from Fig 2 of Borjogin et al. (2013)

The authors even suggest that the level of activity observed during CAS3 may not only resemble the waking state, but might even reflect a heightened state of conscious awareness 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 consciousness metric in humans (see here), to generalise to animals could only ever be speculative. Indeed, research in animals can only ever hint at human experience, including near-death experiences.

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.

Future directions: The obvious next step is to test weather similar patterns of brain activity are observed in humans after clinical death. 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.

Key Reference:

Borjigin, Lee, Liu, Pal, Huff, Klarr, Sloboda, Hernandez, Wang & Mashour (2013) Surge of neurophysiological coherence and connectivity in the dying brain. PNAS

Related references:

Tononi G (2012) Integrated information theory of consciousness: An updated account. Arch Ital Biol 150(2-3):56–90.

Auyong DB, et al. (2010) Processed electroencephalogram during donation after
cardiac death. Anesth Analg 110(5):1428–1432

Related blogs and news articles:

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National Geographic
The Independent

In the news: Decoding dreams with fMRI

Recently Horikawa and colleagues from ATR Computational Neuroscience Laboratories, in Kyoto (Japan), caused a media sensation with the publication of the study in Science that shows first-time proof-of-principle that non-invasive brain scanning (fMRI) can be used to decode dreams. Rumblings were already heard in various media circles after Yuki Kamitani presented their initial findings at the annual meeting of the Society for Neuroscience in New Orleans last year [see Mo Costandi's report]. 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., here, here and here]. The idea of reading people's dreams was always bound to attract a lot of media attention.

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?

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 simultaneous EEG 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.

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.

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.

If it looks like a duck...

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 (support vector machine), 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).

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.      

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?

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 post]. 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 machine-learning language, this could be done using a cross-validated classification 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.

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 (Stokes et al., 2009, J Neurosci). The same approach can be used to test for other coding principles, including high-order properties such as position-invariance (Stokes et al., 2011, NeuroImage), 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.

Many barriers remain for an all-purpose dream decoder

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.

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.

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 here).

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.

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.


For the moment, it is probably better just to keep a dream journal.

Reference:


Horikawa, Tamaki, Miyawaki & Kamitani (2013) Neural Decoding of Visual Imagery During Sleep, Science [here]

In the news: clever coding gets the most out of retinal prosthetics

This is something of an update to a previous post, but I thought interesting enough for its own blog entry. Just out in PNAS, Nirenberg and Pandarinath describe how they mimic the retina’s neural code to improve the effective resolution of an optogenetic prosthetic device (for a good review, see Nature News).

As we have described previously, retinal degeneration affects the photoreceptors (i.e., rod and cone cells), but often spares the ganglion cells that would otherwise carry the visual information to the optic nerve (see retina diagram below). By stimulating these intact output cells, visual information can bypass the damaged retinal circuitry to reach the brain. Although the results from recent clinical trials are promising, restored vision is still fairly modest at best. To put it in perspective, Nirenberg and Pandarinath write:

[current devices enable] "discrimination of objects or letters if they span ∼7 ° of visual angle; this corresponds to about 20/1,400 vision; for comparison, 20/200 is the acuity-based legal definition of blindness in the United States"

Obviously, this poor resolution must be improved upon. Typically, the problem is framed as a limit in the resolution of the stimulating hardware, but Nirenberg and Pandarinath show that software matters too. In fact, they demonstrate that software matters a great deal.

This research focuses on a specific implementation of retinal prosthesis based on optogenetics (for more on approach check out this Guardian article, and for an early empirical demonstration). Basically, intact retinal ganglion cells are injected with a genetically engineered virus that produces a light sensitive protein. These modified cells will now respond to light coming into the eye, just as the rods and cones do in the healthy retina. This approach, although still being developed in mouse models, promises a more powerful and less invasive alternative to electrode arrays previously trialled in humans. But it is not the hardware that is the focus of this research. Rather, Nirenberg and Pandarinath show how the efficacy of the these prosthetic devices critically depends on the type of signal used to activate the ganglion cells. As schematised below, they developed a special type of encoder to convert natural images into a format that more closely matches the neural code expected by the brain. 

The steps from visual input to retinal output proceed as follows: Images enter a device that contains the encoder and a stimulator [a modified minidigital light projector (mini-DLP)]. The encoder converts the images into streams of electrical pulses, analogous to the streams of action potentials that would be produced by the normal retina in response to the same images. The electrical pulses are then converted into light pulses (via the mini-DLP) to drive the ChR2, which is expressed in the ganglion cells.

This neural code is illustrated in the image below: 

The key result of this research paper is a dramatic increase in the amount of information that is transduced to the retinal output cells. They used a neural decoding procedure to quantify the information content in the activity patterns elicited during visual stimulation of a healthy retina, compared to optogenetic activation of ganglion cells in the degenerated retina via encoded or unencoded stimulation. Sure enough, the encoded signals were able to reinstate activity patterns that contained much more information than the raw signals. In a more dramatic, and illustrative, demonstration of this improvement, they used an image reconstruction method to show how the original image (baby's face in panel A) is first encoded by the device (reconstructed in panel B) to activate a pattern of ganglion cells (image-reconstructed in panel C). Clearly, the details are well-preserved, especially in comparison to the image-reconstruction of a non-encoded transduction (in panel D). In a final demonstration, they also found that the experimental mice could track a moving stimulus using the coded signal, but not the raw unprocessed input.

According to James Weiland, ophthalmologist at University of Southern California (quoted in by Geoff Brumfiel Nature News), there has been considerable debate whether it is more important to try to mimic the neural code, or just allow the system to adapt to an unprocessed signal. Nirenberg and Pandarinath argue that clever pre-processing will be particularly important for retinal prosthetics, as there appears to be less plasticity in the visual system than say the auditory system. Therefore, it is essential that researchers crack the neural code of the retina rather than hope the visual system will learn to adapt to an artificial input. The team are optimistic:

"the combined effect of using the code and high-resolution stimulation is able to bring prosthetic capabilities into the realm of normal image representation"

But only time, and clinical trials, will tell.


References:

Bi A, et al. (2006) Ectopic expression of a microbial-type rhodopsin restores visual responses in mice with photoreceptor degeneration. Neuron 50(1):23–33.

Nirenberg and Pandarinath (2012). Retinal prosthetic strategy with the capacity to restore normal vision. PNAS

In the news: Mind Reading

Mind reading tends to capture the headlines. And these days we don't need charlatan mentalists to perform parlour tricks before a faithful audience - we now have true scientific mind reading. Modern brain imaging tools allow us to read the patterns of brain activity that constitute mind... well, sort of. I thought to write this post in response to a recent Nature News Feature on research into methods for reading the minds of patients without any other means of communication. In this post, I consider what modern brain imaging brings to the art of mind reading.

Mind reading as a tool for neuroscience research

First, it should be noted that almost any application of brain imaging in cognitive neuroscience can be thought of as a form of mind reading. Standard analytic approaches test whether we can predict brain activity from the changes in cognitive state (e.g., in statistical parametric mapping). It is straightforward to turn this equation round to predict mental state from brain activity. With this simple transformation, the huge majority of brain imaging studies are doing mind reading. Moreover, a class of analytic methods known as multivariate (or multivoxel) pattern analysis (or classification) have come even closer to mind reading for research purposes. Essentially, these methods rely on a two-stage procedure. The first step is to learn which patterns of brain activity correspond to which cognitive states. Next, these learned relationships are used to predict the cognitive state associated with brain activity. This train/test procedure is strictly "mind reading", but essentially as a by-product.

In fact, the main advantage of this form of mind reading in research neuroscience is that it provides a powerful method for exploring how complex patterns in brain data vary with the experimental condition. Multivariate analysis can also be performed the other way around (by predicting brain activity from behaviour, see here), and similarly, there is no reason why train-test procedures can't be used for univariate analyses. In this type of research, the purpose is not actually to read the mind of cash-poor undergraduates who tend to volunteer for these experiments, but rather to understand the relationship between mind and brain.

Statistical methods for prediction provide a formal framework for this endeavour, and although they are a form of mind reading, it is unlikely to capture the popular imagination once the finer details are explained. Experiments may sometimes get dressed up like a mentalist's parlour trick (e.g., "using fMRI, scientists could read the contents of consciousness"), but such hype invariably leaves those who actually read the scientific paper a bit disappointed by the more banal reality (e.g., "statistical analysis could predict significantly above chance whether participants were seeing a left or right tilted grating"... hardly the Jedi mind trick, but very cool from a neuroscientific perspective), or contribute to paranoid conspiracy theories in those who didn't read the paper, but have an active imagination.

Mind reading as a tool for clinical neuroscience

So, in neuroscientific research, mind reading is most typically used as a convenient tool for studying mind-brain relationships. However, the ability to infer mental states from brain activity has some very important practical applications. For example, in neural prosthesis, internal thoughts are decoded by "mind reading" algorithms to control external devices (see previous post here). Mind reading may also provide a vital line of communication to patients who are otherwise completely unable to control any voluntary movement.

Imagine you are in an accident. You suffer serious brain damage that leaves you with eye blinking as your only voluntary movement for communicating with the outside world. That's bad, very bad in fact - but in time you might perfect this new form of communication, and eventually you might even write a good novel, with sufficient blinking and heroic patience. But now imagine that your brain damage is just a little bit worse, and now you can't even blink your eyes. You are completely locked in, unable to show the world any sign of your conscious existence. To anyone outside, you appear completely without a mind. But inside, your mind is active. Maybe not as sharp and clear as it used to be, but still alive with thoughts, feelings, emotions, hopes and fears. Now mind reading, at any level, becomes more than just a parlour trick.

"It is difficult to imagine a worse experience than to be a functioning mind trapped in a body over which you have absolutely no control" Prof Chris Frith, UCL [source here]

As a graduate student in Cambridge, I volunteered as a control participant in a study conducted by Adrian Owen to read mental states with fMRI for just this kind of clinical application (since published in Science). While I lay in the scanner, I was instructed to either imagine playing tennis or to spatially navigate around a familiar environment. The order was up to me, but it was up to Adrian and his group to use my brain response to predict which of these two tasks I was doing at any given time. I think I was quite bad at spatially navigating, but whatever I did inside my brain was good enough for the team to decode my mental state with remarkable accuracy.

Once validated in healthy volunteers (who, conveniently enough, can reveal which task they were doing inside their head, thus the accuracy of the predictions can be confirmed), Adrian and his team then applied this neuroscientific knowledge to track the mental state of a patient who appeared to be in a persistent vegetative state. When they asked her to imagine playing tennis, her brain response looked just like mine (and other control participants), and when asked to spatially navigate, her brain looked just like other brains (if not mine) engaged in spatial navigation.

In this kind of study, nothing very exciting is learned about the brain, but something else extremely important has happened: someone has been able to communicate for the first time since being diagnosed as completely non-conscious. Adrian and his team have further provided proof-of-principle that this form of mind reading can be applied in other patients to test their level conscious awareness (see here). By following the instructions, some patients were able to demonstrate for the first time a level of awareness that was previously completely undetected. In one further example, they even show that this brain signal can be used to answer some basic yes/no questions.

This research has generated an enormous amount of scientific, clinical and public interest [see his website for examples]. As quoted in a recent Nature New Feature, Adrian has since been "awarded a 7-year Can$10-million Canada Excellence Research Chair and another $10 million from the University of Western Ontario" and "is pressing forward with the help of three new faculty members and a troop of postdocs and graduate students". Their first goal is to develop cheaper and more effective means of using non-invasive methods like fMRI and EEG to restore communication. However, one could also imagine a future for invasive recording methods. Bob Knight's team in Berkeley have been using electrical recording made directly from the brain surface to decode speech signals (see here for a great summary in the Guardian by Ian Sample). Presumably, this kind of method could be considered for patients identified as partially conscious.

See also an interesting interview with Adrian by Mo Constandi in the Guardian

References:
Monti, al. (2010). Willful modulation of brain activity in disorders of consciousness. New England Journal of Medicine
Owen, et al (2006). Detecting awareness in the vegetative state. Science

Pasley,  et al (2012). Reconstructing Speech from Human Auditory Cortex. PLoS Biology

In the news: More neural prosthetics

Last week we heard about the retinal implant, this week is all about the neural prosthetic arm (video). As part of a clinical trial conducted by the BrainGate team, patients suffering long-term tretraplegia (paralysis including all limbs and torso) were implanted with tiny 4x4mm 96-channel microelectrode arrays. Signals from the primary motor cortex were then recorded, and analysed, to decode action commands that could then be used to drive a robotic arm. According to one of the patients:

"At the very beginning I had to concentrate and focus on the muscles I would use to perform certain functions. BrainGate felt natural and comfortable, so I quickly got accustomed to the trial."

Plugging directly into the motor cortex to control a robotic arm could open a whole host of possibilities, if the even larger host of methodological obstacles can be over come. Neuroscientists have become increasingly good at decoding brain signals, especially those controlling action, and are continually fine tuning these skills (see here in the same issue of Nature for another great example of the basic science that ultimately underpins these kinds of clinical applications). The biggest problem, however, is likely to be the bioengineering challenge of developing implants that can read brain activity without damaging neurons over time. The build up of scar tissue around the electrodes will inevitably reduce the quality of the signal. As noted by the authors:

"The use of neural interface systems to restore functional movement will become practical only if chronically implanted sensors function for many years" 

They go on to say that one of their experimental participants had been implanted with their electrode array some 5 years earlier. Although they concede that the quality of the signal had degraded over that time, it was still sufficiently rich to decode purposeful action. They suggest that:

"the goal of creating long-term intracortical interfaces is feasible"

These results are certainly encouraging, however such high-profile trials should not overshadow other excellent research into non-invasive methods for brain computer interface. To avoid neurosurgical procedures has obvious appeal, and would also allow for more flexibility in updating hardware as new developments arise.



References:

Hochberg, Bacher, Jarosiewicz, Masse, Simeral, Vogel, Haddadin, Liu, Cash, van der Smagt & Donoghue. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 485(7398):372-5

Ethier, Oby, Bauman & Miller (2012) Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature, 485(7398):368-71.

In The News: The "Bionic Eye"

It is a big news story in the UK at the moment. Surgeons at the John Radcliffe Hospital in Oxford have implanted UK's first subretinal prosthetic device (see link). This is an exciting development toward restoring useful vision to people suffering retinal degeneration. According to the NHS website, the clinical trial involves a number of patients with retinitis pigmentosa, which is a progressive eye disease affecting photoreceptors (rods and cones). So far, the results are promising. Quoting from the NHS press release:

"When his electronic retina was switched on for the first time, three weeks after the operation, James was able to distinguish light against a black background in both eyes. He is now reported to be able to recognise a plate on a table and other basic shapes, and his vision continues to improve"

Although these effects might seem modest to the sighted, they could provide major improvement in the quality of life for those involved in the trial. To a fully blind patient, even partial vision could dramatically increase their independence. According to the manufacturer, after implantation of the chip the patient’s visual ability should meet the following criteria:

• Orientation in space 
• visual field: 8° - 12° 
• Capacity to see without visual aids (except glasses): at least ability to count fingers, at best ability to recognize faces. 
• Ability to recognize the letters of the alphabet with additional visual aids. 
• Ability to see in surround brightness from 10 Lux to 100.000 Lux.

To achieve all these would certainly make a real difference to a fully blind patient. So, how does the device work? In general, there are two type of retinal implant being developed: epiretinal and subretinal. Both essentially work by converting light energy to electrical energy to stimulate intact retinal cells, which is normally done by damaged photoreceptors (rods and cones). The epiretinal variety consists of an external video camera that transmits a processed signal to the implant, which in turn activates the reintal cells corresponding to the pixelated representation of the image. The subretinal implant, used in this trial, is fitted behind the retina and microphotodiodes directly convert light into electrical impulses to stimulate retinal cells. The principal advantage of the subretinal device, everything is internal to the implant (except for a small power source fitted under the skin). To quote Professor MacLaren:

 

"What makes this unique is that all functions of the retina are integrated into the chip. It has 1,500 light sensing diodes and small electrodes that stimulate the overlying nerves to create a pixellated image. Apart from a hearing aid-like device behind the ear, you would not know a patient had one implanted."



Moreover, by directly stimulating retinal cells rather than ganglion cells results in a more direct and natural correspondence between the implant and the underlying biology. Essentially, this reflects a general trade-off principle in neuroprosthetics. The simplest and most effective devices (e.g., cochlear implants) utilize the existing organisation of primary receptor surfaces, and/or their close neighbours, thereby minimizing the engineering challenge of interfacing with the more complex neural coding schemes. But this only works if those structures remain intact. To by-pass the entire sensory organ and project directly to the cortex is an entirely different game.