Neuroconscience

The latest thoughts, musings, and data in cognitive science and neuroscience.

Short post – my science fiction vision of how science could work in the future

6922_072dSadly I missed the recent #isScienceBroken event at UCL, which from all reports was a smashing success. At the moment i’m just terribly focused on finishing up a series of intensive behavioral studies plus (as always) minimizing my free energy, so it just wasn’t possible to make it. Still, a few were interested to hear my take on things. I’m not one to try and commentate an event I wasn’t at, so instead i’ll just wax poetic for a moment about the kind of Science future i’d like to live in. Note that this has all basically been written down in my self-published article on the subject, but it might bear a re-hash as it’s fun to think about. As before, this is mostly adapted from Clay Shirky’s sci-fi vision of a totally autonomous and self-organizing science.

Science – OF THE FUTURE!

Our scene opens in the not-too distant future, say the year 2030. A gradual but steady trend towards self-publication has lead to the emergence of a new dominant research culture, wherein the vast majority of data first appear as self-archived digital manuscripts containing data, code, and descriptive-yet-conservative interpretations on centrally maintained, publicly supported research archives, prior to publication in traditional journals. These data would be subject to fully open pre-and post-publication peer review focused solely on the technical merit and clarity of the paper.

Having published your data in a totally standardized and transparent format, you would then go on write something more similar to what we currently formulate for high impact journals. Short, punchy, light on gory data details and heavy on fantastical interpretations. This would be your space to really sell what you think makes those data great – or to defend them against a firestorm of critical community comments. These would be submitted to journals like Nature and Science who would have the strictly editorial role of evaluating cohesiveness, general interest, novelty, etc. In some cases, those journals and similar entities (for example, autonomous high-reputation peer reviewing cliques) would actively solicit authors to submit such papers based on the buzz (good or bad) that their archived data had already generated. In principle multiple publishers could solicit submissions from the same buzzworthy data, effectively competing to have your paper in their journal. In this model, publishers must actively seek out the most interesting papers, fulfilling their current editorial role without jeopardizing crucial quality control mechanisms.

Is this crazy? Maybe. To be honest I see some version of this story as almost inevitable. The key bits and players may change, but I truly believe a ‘push-to-repo’ style science is an inevitable future. The key is to realize that even journals like Nature and Science play an important if lauded role, taking on editorial risk to highlight the sexiest (and least probable) findings. The real question is who will become the key players in shaping our new information economy. Will today’s major publishers die as Blockbuster did – too tied into their own profit schemes to mobilize – or will they be Netflix, adapting to the beat of progress?  By segregating the quality and impact functions of publication, we’ll ultimately arrive at a far more efficient and effective science. The question is how, and when.

note: feel free to point out in the comments examples of how this is already becoming the case (some are already doing this). 30 years is a really, really conservative estimate :) 

UPDATED WITH ANSWERS – summary of the major questions [and answers] asked at #LSEbrain about the Bayesian Brain Hypothesis

ok here are the answers! meant to release them last night but was a bit delayed by sleep :)

OK it is about 10pm here and I’ve got an HBM abstract to submit but given that the LSE wasn’t able to share the podcast, i’m just going to quickly summarize some of the major questions brought up either by the speakers or audience during the event.

For those that don’t know, the LSE hosted a brief event tonight exploring the question: “is the brain a predictive machine”, with panelists Paul Fletcher, Karl Friston, Demis Hassabis, Richard Holton and chaired by Benedetto De Martino. I enjoyed the event as it was about the right length and the discussion was lively. For those familiar with Bayesian brain/predictive coding/FEP there wasn’t much new information, but it was cool to see an outside audience react.

These were the principle questions that came up in the course of the event. Keep in mind these are just reproduced from my (fallible) memory:

  • What does it mean if someone acts, thinks, or otherwise behaves irrationally/non-optimally. Can their brain still be Bayesian at a sub-personal level?
    • There were a variety of answers to this question, with the most basic being that optimal behavior depends on ones prior, so someone with a mental disorder or poor behavior may be acting optimally with respect to their priors. Karl pointed out that that this means optimal behavior really is different for every organism and person, rendering the notion of optimal trivial.
  • Instead of changing the model, is it possible for the brain to change the world so it fits with our model of it?
    • Yes, Karl calls this active inference and it is a central part of his formulation of the Bayesian brain. Active inference allows you to either re-sample or adjust the world such that it fits with your model, and brings in a kind of strong embodiment to the Bayesian brain. This is because the kinds of actions  (and perceptions) one can engage in are shaped by the body and internal states,
  • Where do the priors come from?
    • Again the answer from Karl – evolution. According to the FEP, organisms who survive do so in virtue of their ability to minimize free energy (prediction error). This means that for Karl evolution ‘just is the refinement and inheritance of our models of the world'; our brains reflect the structure of the world which is then passed on through natural selection and epigenetic mechanisms.
  • Is the theory falsifiable and if so, what kind of data would disprove it?
    • From Karl – ‘No. The theory is not falsifiable in the same sense that Natural Selection is not falsifiable’. At this there were some roars from the crowd and philosopher Richard Holton was asked how he felt about this statement. Richard said he would be very hesitant to endorse a theory that claimed to be non-falsifiable.
  • Is it possible for the brain to ‘over-fit’ the world/sensory data?
    • Yes, from Paul we heard that this is a good description of what happens in psychotic or other mental disorders, where an overly precise belief might resist any attempts to dislodge it or evidence to the contrary. This lead back into more discussion of what it means for an organism to behave in a way that is not ‘objectively optimal’.
  • If we could make a Bayesian deep learning machine would it be conscious, and if so what rights should we give it?
    • I didn’t quite catch Demis response to this as it was quite quick and there was a general laugh about these types of questions coming up.
  • How exactly is the brain Bayesian? Does it follow a predictive coding, approximate, or variational Bayesian implementation?
    • Here there was some interesting discussion from all sides, with Karl saying it may actually be a combination of these methods or via approximations we don’t yet understand. There was a lot of discussion about why Deep Brain doesn’t implement a Bayesian scheme in their networks, and it was revealed that it is because hierarchical Bayesian inference is currently too computationally demanding for such applications. Karl picked up on this point to say that the same is true of the human brain; the FEP outlines some general principles but we are still far from understanding how the brain actually approximates Bayesian inference.
  • Can conscious beliefs, or decisions in the way we typically think of them, be thought of in a probabilistic way?’
    • Karl: ‘Yes’
    • Holton: Less sure
    • Panel: this may call for multiple models, binary vs discrete, etc
    • Karl redux: isn’t it interesting how now we are increasingly reshaping the world to better model our predictions, i.e. using external tools in place of memory, navigation, planning, etc (i.e. extended cognition)

There were other small bits of discussion, particularly concerning what it means for an agent to be optimal or not, and the relation of explicit/conscious states to a subpersonal Bayesian brain, but I’m afraid I can’t recall them in enough detail to accurately report them. Overall the discussion was interesting and lively, and I presume there will be some strong opinions about some of these. There was also a nice moment where Karl repeatedly said that the future of neuroscience was extended and enactive cognition. Some of the discussion between the panelist was quite interesting, particularly Paul’s views on mental disorders and Demis talking about why the brain might engage in long-term predictions and imagination (because collecting real data is expensive/dangerous).

Please write in the comments if I missed anything. I’d love to hear what everyone thinks about these. I’ve got my opinions particularly about the falsification question, but I’ll let others discuss before stating them.

[VIDEO] Mind-wandering, meta-cognition, and the function of consciousness

Hey everyone! I recently did an interview for Neuro.TV covering some of my past and current research on mind-wandering, meta-cognition, and conscious awareness. The discussion is very long and covers quite a diversity of topics, so I thought i’d give a little overview here with links to specific times.

For the first 15 minutes, we focus on general research in meta-cognition, and topics like the functional and evolutionary signifigance of metacognition:

We then begin to move onto specific discussion about mind-wandering, around 16:00:

I like our discussion as we quickly get beyond the overly simplistic idea of ‘mind-wandering’ as just attentional failure, reviewing the many ways in which it can drive or support meta-cognitive awareness. We also of course briefly discuss the ‘default mode network’ and the (misleading) idea that there are ‘task positive’ and ‘task negative’ networks in the brain, around 19:00:

Lots of interesting discussion there, in which I try to roughly synthesize some of the overlap and ambiguity between mind-wandering, meta-cognition, and their neural correlates.

Around 36:00 we start discussing my experiment on mind-wandering variability and error awareness:

A great experience in all, and hopefully an interesting video for some! Be sure to support the kickstarter for the next season of Neuro.TV!

JF also has a detailed annotation on the brainfacts blog for the episode:

“0:07″ Introduction
“0:50″ What is cognition?
“4:45″ Metacognition and its relation to confidence.
“10:49″ What is the difference between cognition and metacognition?
“14:07″ Confidence in our memories; does it qualify as metacognition?
“18:34″ Technical challenges in studying mind-wandering scientifically and related brain areas.
“25:00″ Overlap between the brain regions involved in social interactions and those known as the default-mode network.
“29:17″ Why does cognition evolve?
“35:51″ Task-unrelated thoughts and errors in performance.
“50:53″ Tricks to focus on tasks while allowing some amount of mind-wandering.

What’s the causal link dissociating insula responses to salience and bodily arousal?

Just reading this new paper by Lucina Uddin and felt like a quick post. It is a nice review of one of my favorite brain networks, the ever present insular cortex and ‘salience network’ (thalamus, AIC, MCC). As we all know AIC activation is one of the most ubiquitous in our field and generally shows up in everything. Uddin advances the well-supported idea that in addition to being sensitive to visceral, autonomic, bodily states (and also having a causal influence on them), the network responds generally to salient stimuli (like oddballs) across all sensory modalities. We already knew this but a thought leaped to my mind; what is the order of causation here? If the AIC responds to and causes arousal spikes, are oddball responses driven by the novelty of the stimuli or by a first order evoked response in the body? Your brainstem, spinal cord, and PNS are fully capable of creating visceral responses to unexpected stimuli. How can we dissociate ‘dry’ oddball responses from evoked physiological responses? It seems likely that arousal spikes accompany anything unexpected and that salience itself doesn’t really dissociate AIC responses from a more general role of bodily awareness. Recent studies show that oddballs evoke pupil dilation, which is related to arousal.

Check out this figure:

fig1

Clearly AIC and ACC not only receive physiological input but also can directly cause phsyio outputs. I’m immediately reminded of an excellent review by Markus Ullsperger and colleagues, where they run into a similar issue trying to work out how arousal cues contribute to conscious error awareness. Ultimately Ullsperger et al conclude that we can’t really dissociate whether arousal cues cause error awareness or error-awareness causes arousal spikes. This seems to also be true for a general salience account.

ulls

How can we tease these apart? It seems like we’d need to somehow both knock out and cause physiological responses during the presence and absence of salient stimuli. I’m not sure how we could do this – maybe de-afferentiated patients could get us part of the way there. But a larger problem looms also: the majority of findings cited by Uddin (and to a lesser extent Ullsperger) come from fMRI. Indeed, the original Seeley et al “salience network” paper (one of the top 10 most cited papers in neuroscience) and the original Critchley insula-interoception papers (also a top ten paper) is based on fMRI. Given that these areas are also heavily contaminated by pulse and respiration artifacts, how can we work out the causal loop between salience/perception and arousal? If a salient cue causes a pulse spike then it might also cause a corresponding BOLD artifact. It might be that there is a particularly non-artefactual relationship between salient things and arousal but currently we can’t seem to work out the direction of causation. Worse, it is possible the process driving the artifacts themselves are crucial for ‘salience’ computation, which would mean physio-correction would obscure these important relationships! A tough cookie indeed. Lastly, we’ll need to go beyond the somewhat psychological label of ‘salience’ if we really want to work out these issues. For my money, I think an account based on expected precision fits nicely with the pattern of results we see in these areas, providing a computational mechanism for ‘salience’.

In the end I suspect this is going be one for the direct recording people to solve. If you’ve got access to insula implantees, let me know! :D

Note: folks on twitter said they’d like to see more of the cuff posts – here you go! This post was written in a flurry of thought in about 30 minutes, so please excuse any snarfs! 

Twitter recommends essential reading in pupilometry

Not sure why WordPress is refusing to accept the Storify embed, but click here for some excellent suggestions on reading in pupilometry:

https://storify.com/neuroconscience/twitter-recommendations-for-essential-reading-in-p

Top 200 terms in cognitive neuroscience according to neurosynth

Tonight I was playing around with some of the top features in neurosynth (the searchable terms with the highest number of studies containing that term). You can find the list here, just sort by the number of studies. I excluded the top 3 terms which are boring (e.g. “image”, “response”, and “time”)  and whose extremely high weights would mess up the wordle. I then created a word-cloud weighted so that the size reflects the number of studies for each term.

Here are the top 200 terms sized according to number times reported in neurosynth’s 5809 indexed fMRI studies:

wordle

Pretty neat! These are the 200 terms the neurosynth database has the most information on, and is a pretty good overview of key concepts and topics in our field! I am sure there is something useful for everyone in there :D

Direct link to the wordle:

Wordle: neurosynth

Neurovault: a must-use tool for every neuroimaging paper!

Something that has long irked me about cognitive neuroscience is the way we share our data. I still remember the very first time I opened a brain imaging paper and was struck dumbfounded by the practice of listing activation results in endless p-value tables and selective 2D snapshots. How could anyone make sense of data this way? Now having several years experience creating such papers, I am only more dumbfounded that we continue to present our data in this way. What purpose can be served by taking a beautiful 3-dimensional result and filtering it through an awkward foci ‘photoshoot’? While there are some standards you can use to improve the 2D presentation of 3D brain maps, for example showing only peak activation and including glass-brains, this is an imperfect solution – ultimately the best way to assess the topology of a result is by directly examining the full 3D result.

Just imagine how improved every fMRI paper would be, if instead of a 20+ row table and selective snapshot, results were displayed in a simple 3D viewing widget right in the paper? Readers could assess the underlying effects at whatever statistical threshold they feel is most appropriate, and PDF versions could be printed at a particular coordinate and threshold specified by the author. Reviewers and readers alike could get a much fuller idea of the data, and meta-analysis would be vastly improved by the extensive uploading of well-categorized contrast images. More-over, all this can be easily achieved while excluding worries about privacy and intellectual property, using only group-level contrast images, which are inherently without identifying features and contain only those effects included in the published manuscript!

Now imagine my surprise when I learned that thanks to Chris Gorgolewksi and colleagues, all of this is already possible! Chris pioneered the development of neurovault.org, an extremely easy to use data sharing site backed by the International Neuroinformatics Coordinating Facility. To use it, researchers simply need to create a new ‘collection’ for their study and then upload whatever images they like. Within about 15 minutes I was able to upload both the T- and contrast-images from my group level analysis, complete with as little or as much meta-data as I felt like including. Collections can be easily linked to paper DOIs and marked as in-review, published, etc. Collections and entries can be edited or added to at any time, and the facilities allow quick documentation of imaging data at any desired level, from entire raw imaging datasets to condition-specific group contrast images. Better still, neurovault seamlessly displays these images on a 3D MNI standard brain with flexible options for thresholding, and through a hookup to neurosynth.org can even seamlessly find meta-analytic feature loadings for your images! Check out these t-map display and feature loadings for the stimulus intensity contrast for my upcoming somatosensory oddball paper, which correctly identified the modality of stimulation!

T-map in the neurovault viewer.

T-map in the neurovault viewer.

Decoded features for my contrast image.

Decoded features for my contrast image, with accurate detection of stimulation modality!

Neurovault.org doesn’t yet support embedding the viewer, but it is easy to imagine that with collaboration from publishers, future versions could be embedded directly within HTML full-text for imaging papers. For now, the site provides the perfect solution for researchers looking to make their data available to others and to more fully present their results, simply by providing supplementary links either to the neurovault collection or directly to individual viewer results. This is a tool that everyone in cognitive neuroscience should be using – I fully intend to do so in all future papers!

Is there a ‘basement’ for quirky psychological research?

Beware the Basement!

Beware the Basement!

 One thing I will never forget from my undergraduate training in psychology was the first lecture of my personality theory class. The professor started the lecture by informing us that he was quite sure that of the 200+ students in the lecture hall, the majority of us were probably majoring in psychology because we thought it would be neat to study sex, consciousness, psychedelics, paranormal experience, meditation, or the ilk. He then informed us this was a trap that befell almost all new psychology students, as we were all drawn to the study of the mind by the same siren call of the weird and wonderful human psyche. However he warned, we should be very, very careful not to reveal these suppressed interests until we were well established (I’m assuming he meant tenured) researchers- otherwise we’d risk being thrown into the infamous ‘basement of psychology’, never to be heard from again.

This colorful lecture really stuck with me through the years; I still jokingly refer to the basement whenever a more quirky research topic comes up. Of course I did a pretty poor job of following this advice, seeing as my first project as a PhD student involved meditation, but nonetheless I have repressed an academic interest in more risque topics throughout my career. And i’m not really actively avoiding them for fear of being placed in the basement – i’m more just following my own pragmatic research interests, and waiting for some day when I have more time and freedom to follow ideas that don’t directly tie into the core research line I’m developing.

But still. That basement. Does it really exist? In a world where papers about having full bladders renders us more politically conservative can make it into prestigious journals, or where scientists scan people having sex inside a scanner just to see what happens, or where psychologists seriously debate the possibility of precognition – can anything really be taboo? Or can we still distinguish from these flightier topics a more serious avenue of research? And what should be said about those who choose such topics?

Personally I think the idea of a ‘basement’ is largely a hold-over from the heyday of behaviorism, when psychologists were seriously concerned about positioning psychology as a hard science. Cognitivism has given rise to an endless bevy of serious topics that would have once been taboo; consciousness, embodiment, and emotion to name a few. Still, in the always-snarky twittersphere, one can’t but help feel that there is still a certain amount of nose thumbing at certain topics.

I think really, in the end, it’s not the topic so much as the method. Chris Frith once told me something to the tune of ‘in [cognitive neuroscience] all the truly interesting phenomenon are beyond proper study’. We know the limitations of brain scans and reaction times, and so tend to cringe a bit when someone tries to trot out the latest silly-super-human special interest infotainment paper.

What do you think? Is there a ‘basement’ for silly research? And if so, what defines what sorts of topics should inhabit its dank confines?

We the Kardashians are Democratizing Science

I had a good laugh this weekend at a paper published to Genome Biology. Neil Hall, the author of the paper and well-established Liverpool biologist, writes that in the brave new era of social media, there “is a danger that this form of communication is gaining too high a value and that we are losing sight of key metrics of scientific value, such as citation indices.” Wow, what a punchline! According to Neil, we’re in danger of forgetting that tweets and blogposts are, according to him, the worthless gossip of academia. After all, who reads Nature and Science these days?? I know so many colleagues getting big grants and tenure track jobs just over their tweets! Never mind that Neil himself has about 11 papers published in Nature journals – or perhaps we are left to sympathize with the poor, untweeted author? Outside of bitter sarcasm, the article is a fun bit of satire, and I’d like to think charitably that it was aimed not only at ‘altmetrics’, but at the metric enterprise in general. Still, I agree totally with Kathryn Clancy that the joke fails insofar as it seems to be ‘punching down’ at those of us with less established CVs than Neil, who take to social media in order to network and advance our own fledgling research profiles. I think it also belies a critical misapprehension of how social media fits into the research ecosystem common among established scholars. This sentiment is expressed rather precisely by Neil when discussing his Kardashian index:

The Kardashian Index

The Kardashian Index

“In an age dominated by the cult of celebrity we, as scientists, need to protect ourselves from mindlessly lauding shallow popularity and take an informed and critical view of the value we place on the opinion of our peers. Social media makes it very easy for people to build a seemingly impressive persona by essentially ‘shouting louder’ than others. Having an opinion on something does not make one an expert.”

So there you have it. Twitter equals shallow popularity. Never mind the endless possibilities of having seamless networked interactions with peers from around the world. Never mind sharing the latest results, discussing them, and branching these interactions into blog posts that themselves evolve into papers. Forget entirely that without this infosphere of interaction, we’d be left totally at the whims of Impact Factor to find interesting papers among the thousands published daily. What it’s really all about is building a “seemingly impressive persona” by “shouting louder than others”. What then does constitute effective scientific output, Neil? The answer it seems – more high impact papers:

“I propose that all scientists calculate their own K-index on an annual basis and include it in their Twitter profile. Not only does this help others decide how much weight they should give to someone’s 140 character wisdom, it can also be an incentive – if your K-index gets above 5, then it’s time to get off Twitter and write those papers.”

Well then, I’m glad we covered that. I’m sure there were many scientists or scholars out there who amid the endless cycle of insane job pressure, publish or perish horse-racing, and blood feuding for grants thought, ‘gee I’d better just stop this publishing thing entirely and tweet instead’. And likewise, I’m sure every young scientist looks at ‘Kardashians’ and thinks, ‘hey I’d better suspend all critical thinking, forget all my training, and believe everything this person says’. I hope you can feel me rolling my eyes.  Seriously though – this represents a fundamental and common misunderstanding of the point of all this faffing about on the internet. Followers, impact, and notoriety are all poorly understood side-effects of this process; they are neither the means nor goal. And never mind those less concrete (and misleading) contributions like freely shared code, data, or thoughts – the point here is to blather and gossip!

While a (sorta) funny joke, it is this point that is done the most disservice by Neil’s article. We (the Kardashians) are democratizing science. We are filtering the literally unending deluge of papers to try and find the most outrageous, the most interesting, and the most forgotten, so that they can see the light of day beyond wherever they were published and forgotten. We seek these papers to generate discussion and to garner attention where it is needed most. We are the academy’s newest, first line of defense, contextualizing results when the media runs wild with them. We tweet often because there is a lot to tweet, and we gain followers because the things we tweet are interesting. And we do all of this without the comfort of a lofty CV or high impact track record, with little concrete assurance that it will even benefit us, all while still trying to produce the standard signs of success. And it may not seem like it now – but in time it will be clear that what we do is just as much a part of the scientific process as those lofty Nature papers. Are we perfect? No. Do we sometimes fall victim to sensationalism or crowd mentality? Of course – we are only fallible human beings, trying to find and create utility within a new frontier. We may not be the filter science deserves – but we are the one it needs. Wear your Kardshian index with pride.

oh BOLD where art thou? Evidence for a “mm-scale” match between intracortical and fMRI measures.

A frequently discussed problem with functional magnetic resonance imaging is that we don’t really understand how the hemodynamic ‘activations’ measured by the technique relate to actual neuronal phenomenon. This is because fMRI measures the Blood-Oxygenation-Level Dependent (BOLD) signal, a complex vascular response to neuronal activity. As such, neuroscientists can easily get worried about all sorts of non-neural contributions to the BOLD signal, such as subjects gasping for air, pulse-related motion artefacts, and other generally uninteresting effects. We can even start to worry that out in the lab, the BOLD signal may not actually measure any particular aspect of neuronal activity, but rather some overly diluted, spatially unconstrained filter that simply lacks the key information for understanding brain processes.

Given that we generally use fMRI over neurophysiological methods (e.g. M/EEG) when we want to say something about the precise spatial generators of a cognitive process, addressing these ambiguities is of utmost importance. Accordingly a variety of recent papers have utilized multi-modal techniques, for example combining optogenetics, direct recordings, and FMRI, to assess particularly which kinds of neural events contribute to alterations in the BOLD signal and it’s spatial (mis)localization. Now a paper published today in Neuroimage addresses this question by combining high resolution 7-tesla fMRI with Electrocorticography (ECoG) to determine the spatial overlap of finger-specific somatomotor representations captured by the measures. Starting from the title’s claim that “BOLD matches neuronal activity at the mm-scale”, we can already be sure this paper will generate a great deal of interest.

From Siero et al (In Press)

As shown above, the authors managed to record high resolution (1.5mm) fMRI in 2 subjects implanted with 23 x 11mm intracranial electrode arrays during a simple finger-tapping task. Motor responses from each finger were recorded and used to generate somatotopic maps of brain responses specific to each finger. This analysis was repeated in both ECoG and fMRI, which were then spatially co-registered to one another so the authors could directly compare the spatial overlap between the two methods. What they found appears at first glance, to be quite impressive:
From Siero et al (In Press)

Here you can see the color-coded t-maps for the BOLD activations to each finger (top panel, A), the differential contrast contour maps for the ECOG (middle panel, B), and the maximum activation foci for both measures with respect to the electrode grid (bottom panel, C), in two individual subjects. Comparing the spatial maps for both the index and thumb suggests a rather strong consistency both in terms of the topology of each effect and the location of their foci. Interestingly the little finger measurements seem somewhat more displaced, although similar topographic features can be seen in both. Siero and colleagues further compute the spatial correlation (Spearman’s R) across measures for each individual finger, finding an average correlation of .54, with a range between .31-.81, a moderately high degree of overlap between the measures. Finally the optimal amount of shift needed to reduce spatial difference between the measures was computed and found to be between 1-3.1 millimetres, suggesting a slight systematic bias between ECoG and fMRI foci.

Are ‘We the BOLD’ ready to breakout the champagne and get back to scanning in comfort, spatial anxieties at ease? While this is certainly a promising result, suggesting that the BOLD signal indeed captures functionally relevant neuronal parameters with reasonable spatial accuracy, it should be noted that the result is based on a very-best-case scenario, and that a considerable degree of unique spatial variance remains for the two methods. The data presented by Siero and colleagues have undergone a number of crucial pre-processing steps that are likely to influence their results: the high degree of spatial resolution, the manual removal of draining veins, the restriction of their analysis to grey-matter voxels only, and the lack of spatial smoothing all render generalizing from these results to the standard 3-tesla whole brain pipeline difficult. Indeed, even under these best-case criteria, the results still indicate up to 3mm of systematic bias in the fMRI results. Though we can be glad the bias was systematic and not random– 3mm is still quite a lot in the brain. On this point, the authors note that the stability of the bias may point towards a systematic miss-registration of the ECoG and FMRI data and/or possible rigid-body deformations introduced by the implantation of the electrodes), issues that could be addressed in future studies. Ultimately it remains to be seen whether similar reliability can be obtained for less robust paradigms than finger wagging, obtained in the standard sub-optimal imaging scenarios. But for now I’m happy to let fMRI have its day in the sun, give or take a few millimeters.

Siero, J. C. W., Hermes, D., Hoogduin, H., Luijten, P. R., Ramsey, N. F., & Petridou, N. (2014). BOLD matches neuronal activity at the mm scale: A combined 7T fMRI and ECoG study in human sensorimotor cortex. NeuroImage. doi:10.1016/j.neuroimage.2014.07.002

 

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