Neuroskeptic (@Neuro_Skeptic)

April 10, 2012 at 4:37 pm  (Edit)

Good post. I would add a #3, it’s kind of an aspect of #2 although important enough to stand alone -

Make sure you are one of the pilot subjects. It’s amazing what kind of things you notice when you’re actually in the scanner that you never otherwise would – anything from the fact that the stimuli aren’t very visible, to the fact that the sequence you’re using makes the bed shake, to the fact that the task is just so long & boring that you fall asleep by the end (which is so much easier in the scanner than when you’re sitting up at a computer, which is when you probably piloted the task!)

If you’re not MRI safe, get a trusted fellow researcher to do it. But never assume that non-scientist volunteers will tell you these things because they don’t (I think because they don’t want to look stupid by questioning your authority.)

http://psychsciencenotes.blogspot.co.uk/2012/03/field-spotters-guide-to-embodied.html

Embodied cognition: A field spotter’s guide

Question 1: Does the paper claim to be an example of embodied cognition?

If yes, it is probably not embodied cognition. I’ve never been entirely sure why this is, but work that is actually about embodiment rarely describes itself as such. I think it’s because embodiment is the label that’s emerged to describe work from a variety of disciplines that, at the time, wasn’t about pushing any coherent agenda, and so the work often didn’t know at the time that it was embodied cognition.

This of course is less true now embodiment is such a hot topic, so what else do I look for?

Question 2:  What is the key psychological process involved in solving the task?

Embodied cognition is, remember, the radical hypothesis that we solve tasks using resources spanning our brain, bodies and environments coupled together via perception. If the research you are reading is primarily investigating a process that doesn’t extend beyond the brain (e.g. a mental number line, or a thought about the future) then it isn’t embodiment. For example, in the leaning to the left example, the suggestion was that we estimate the magnitude of things by placing them on a mental number line, and that the way we are leaning makes different parts of that number line easier to access than others (e.g. leaning left makes the smaller numbers more accessible). The key process is the mental number line, which resides solely in the brain and is hypothesised to exist to solve a problem (estimating the magnitude of things) in a manner that doesn’t require anything other than a computing brain. This study is therefore not about embodiment.

Question 3: What is the embodied bit doing?

There’s a related question that comes up, then. In papers that aren’t actually doing embodied cognition, the body and the environment only have minor, subordinate roles. Leaning to the left merely biases our access to the mental number line; thinking about the future has a minor effect on bodily sway. The important bit is still the mental stuff – the cognitive process presumably implemented solely in the brain. If the non-neural or non-cognitive elements are simply being allowed to tweak some underlying mental process, rather than play a critical role in solving the task, it’s not embodiment.

Neurovascular coupling and the BOLD signal: a match not made in heaven

An important line of research in neuroimaging focuses on noise in fMRI signals. The essential problem of fMRI is that, while it provides decent spatial resolution, the data is acquired slowly and indirectly via the blood-oxygenation level dependent (BOLD) signal. The BOLD signal is messy, slow, and extremely complex in its origins. Although we typically assume increasing BOLD signal equals greater neural activity, the details of just what kind of activity (e.g. excitatory vs inhibitory, post-synaptic vs local field) are murky at best. Advancements in multi-modal and optogenetic imaging hold a great deal of promise regarding the signal’s true nature, but sadly we are currently at a “best guess” level of understanding. This weakness means that without careful experimental design, it can be difficult to rule out non-neural contributors to our fMRI signal. Setting aside the worry about what neural activity IS measured by BOLD signal, there is still the very real threat of non-neural sources like respiration and cardiovascular function confounding the final result. This is a whole field of research in itself, and is far too complex to summarize here in its entirety. The basic issue is quite simple though.

End-tidal C02, respiration, and the BOLD Signal

In a nutshell, the BOLD signal is thought to measure downstream changes in cerebral blood-flow (CBF) in response to neural activity. This relationship, between neural firing and blood flow, is called neurovascular coupling and is extremely complex, involving astrocytes and multiple chemical pathways. Additionally, it’s quite slow: typically one observes a 3-5 second delay between stimulation and BOLD response. This creates our first noise-related issue; the time between each ‘slice’ of the brain, or repetition time (TR), must be optimized to detect signals at this frequency. This means we sample from our participant’s brain slowly. Typically we sample every 3-5 seconds and construct our paradigms in ways that respect the natural time lag of the BOLD signal. Stimulate too fast, and the vasculature doesn’t have time to respond. Stimulation frequency also helps prevent our first simple confound: our pulse and respiration rates tend oscillate at slightly slower frequencies (approximately every 10-15 seconds). This is a good thing, and it means that so long as your design is well controlled (i.e. your events are properly staggered and your baseline is well defined) you shouldn’t have to worry too much about confounds. But that’s our first problematic assumption; consider for example when one’s paradigms use long blocks of obscure things like “decide how much you identify with these stimuli”. If cognitive load differs between conditions, or your groups (for example, a PTSD and a control group) react differently to the stimuli, respiration and pulse rates might easily begin to overlap your sampling frequency, confounding the BOLD signal.

But you say, my experiment is well controlled, and there’s no way my groups are breathing THAT differently! Fair enough, but this leads us to our next problem: end tidal CO₂. Without getting into the complex physiology, end-tidal CO₂ is a by-product of respiration. When you hold your breath, CO₂ blood levels rise dramatically. CO₂ is a potent vasodilator, meaning it opens blood vessels and increases local blood flow. You’ve probably guessed where I’m going with this: hold your breath in the fMRI and you get massive alterations in the BOLD signal. Your participants don’t even need to match the sampling frequency of the paradigm to confound the BOLD; they simply need to breath at slightly different rates in each group or condition and suddenly your results are full of CO₂ driven false positives! This is a serious problem for any kind of unconstrained experimental design, especially those involving poorly conceptualized social tasks or long periods of free activity. Imagine now that certain regions of the brain might respond differently to levels of CO_2.

This image is from Change & Glover’s paper, “Relationship between respiration, end-tidal CO₂, and BOLD signals in resting-state fMRI”. Here they measure both CO₂ and respiration frequency during a standard resting-state scan. The image displays the results of group-level regression of these signals with BOLD. I’ve added circles in blue around the areas that respond the strongest. Without consulting an atlas, we can clearly see that bilateral anterior insula extending upwards into parietal cortex, anterior cingulate, and medial-prefrontal regions are hugely susceptible to respiration and CO_2. This is pretty damning for resting-state fMRI, and makes sense given that resting state fluctuations occur at roughly the same rate as respiration. But what about well-controlled event related designs? Might variability in neurovascular coupling cause a similar pattern of response? Here is where Di et al’s paper lends a somewhat terrifying result:

Di et al recently investigated the role of vascular confounds in fMRI by administrating a common digit-symbol substitution task (DSST), a resting state, and a breath-holding paradigm. Signals related to resting-state and breath-holding were then extracted and entered into multiple regression with the DSST-related activations. This allowed Di et al to estimate what brain regions were most influenced by low-frequency fluctuation (ALFF, a common resting state measure) and purely vascular sources (breath-holding). From the figure above, you can see that regions marked with the blue arrow were the most suppressed, meaning the signal explained by the event-related model was significantly correlated with the covariates, and in red where the signal was significantly improved by removal of the covariates. The authors conclude that “(results) indicated that the adjustment tended to suppress activation in regions that were near vessels such as midline cingulate gyrus, bilateral anterior insula, and posterior cerebellum.” It seems that indeed, our old friends the anterior insula and cingulate cortex are extremely susceptible to neurovascular confound.

What does this mean for cognitive neuroscience? For one, it should be clear that even well-controlled fMRI designs can exhibit such confounds. This doesn’t mean we should throw the baby out with the bathwater though; some designs are better than others. Thankfully it’s pretty easy to measure respiration with most scanners, and so it is probably a good idea at minimum to check if one’s experimental conditions do indeed create differential respiration patterns. Further, we need to be especially cautious in cases like meditation or clinical fMRI, where special participant groups may have different baseline respiration rates or stronger parasympathetic responses to stimuli. Sadly, I’m afraid that looking back, these findings greatly limit our conclusions in any design that did not control for these issues. Remember that the insula and ACC are currently cognitive neuroscience’s hottest regions. I’m not even going to get into resting state, where these problems are all magnified 10 fold. I’ll leave you with this image from neuroskeptic, estimating the year’s most popular brain regions:

Are those spikes publication fads, every-task regions, or neurovascular artifacts? You be the judge.

Online, things can seem a bit more pessimistic. Snarky methods blogs dedicated to revealing the worst in field tend to do well, and nearly any social-media savy neurogeek will lament the depressing state of science journalism and the brain. While I am also tired of incessantly phrenological, blob-obsessed reports (“research finds god spot in the brain, are your children safe??”) I think we share some of the blame for not communicating properly about what interests and challenges us. For me, some of the most exciting areas of research are those concerning getting straight about what our measurements mean- see the debates over noise in resting state or the neural underpinnings of the BOLD signal for example. Yet these issues are often reported as dry methodological reports, the writers themselves seemingly bored with the topic.

We need to do a better job illustrating to people just how complex and infantile our field is. The big, sexy issues are methodological in nature. They’re also phenomenological in nature. Right now neuroscience is struggling to define itself, unsure if we should be asking our subjects how they feel or anesthetizing them. I believe that if we can illustrate just how tenuous much of our research is, including the really nagging problems, the public will better appreciate seemingly nuanced issues like rest-stimulus interaction and noise-regression.

With that in mind- what are your most exciting questions, right now? What nagging thorn ails you at all steps in your research?

For me, the most interesting and nagging question is, what do people do when we ask them to do nothing? I’m talking about rest-stimulus interaction and mind wandering. There seem to be two prevailing (pro-resting state) views: that default mode network-related activity is related to subjective mind-wandering, and/or that it’s a form of global, integrative, stimulus independent neural variability. On the first view, variability in participants ability to remain on-task drive slow alterations in behavior and stimulus-evoked brain activity. On the other, innate and spontaneous rhythms synchronize large brain networks in ways that alter stimulus processing and enable memory formation. Either way, we’re left with the idea that a large portion of our supposedly well-controlled, stimulus-related brain activity is in fact predicted by uncontrolled intrinsic brain activity. Perhaps even defined by it! When you consider that all this is contingent on the intrinsic activity being real brain activity and not some kind of vascular or astrocyte-driven artifact, every research paradigm becomes a question of rest-stimulus interaction!

So neuroscientists, what keeps you up at night?

“Meditation experience is associated with differences in default mode network activity and connectivity“

Abstract:

“Many philosophical and contemplative traditions teach that “living in the moment” increases happiness. However, the default mode of humans appears to be that of mind-wandering, which correlates with unhappiness, and with activation in a network of brain areas associated with self-referential processing. We investigated brain activity in experienced meditators and matched meditation-naive controls as they performed several different meditations (Concentration, Loving-Kindness, Choiceless Awareness). We found that the main nodes of the default mode network(medial prefrontal and posterior cingulate cortices) were relatively deactivated in experienced meditators across all meditation types. Furthermore, functional connectivity analysis revealed stronger coupling in experienced meditators between the posterior cingulate, dorsal anterior cingulate, and dorsolateral prefrontal cortices (regions previously implicated in self- monitoring and cognitive control), both at baseline and during meditation. Our findings demonstrate differences in the default-mode network that are consistent with decreased mind-wandering. As such, these provide a unique understanding of possible neural mechanisms of meditation.”

Summary:

Aims: 9/10

Methods: 5/10

Interpretation: 7/10

Importance/Generalizability: 4/10

Overall: 6.25/10

The good: simple, clear cut design, low amount of voodoo, relatively sensible findings

The bad: lack of behavioral co-variates to explain neural data, yet another cross-sectional design

The ugly: prominent reporting of uncorrected findings, comparison of meditation-naive controls to practitioners using meditation instructions (failure to control task demands).

Take-home: Some interesting conclusions, from a somewhat tired and inconclusive design. Poor construction of baseline condition leads to a shot-gun spattering of brain regions with a few that seem interesting given prior work. Let’s move beyond poorly controlled cross-sections and start unravelling the core mechanisms (if any) involved in mindfulness.

Extended Review:
Although this paper used typical GLM and functional connectivity analyses, it loses points in several areas. First, although the authors repeatedly suggest that their “relative paucity of findings” may be “driven by the sensitivity of GLM analysis to fluctuations at baseline… and since meditation practitioners may be (meditating) at baseline…” the contrast would be weak. However, I will side with Jensen et al (2011) here in saying: Meditation naive controls receiving less than 5 minutes of instruction in “focused attention, loving-kindness and choiceless awareness” are simply no controls at all. The argument that the inability of the GLM to detect differences that are quite obviously confounded by a lack of an appropriately controlled baseline is galling at best. This is why we use a GLM-approach; it’s senseless to make conclusions about brain activity when your baseline is no baseline at all. Telling meditation-naive controls to utilize esoteric cultural practices of which they have only just been introduced too, and then comparing that to highly experienced practitioners is a perfect storm of cognitive confusion and poorly controlled demand characteristic. Further, I am disappointed in the review process that allowed the following statement “We found a similar pattern in the medial prefrontal cortex (mPFC), another primary node of the DMN, although it did not survive whole-brain correction for signifigance” followed by this image:

These results are then referred to repeatedly in the following discussion. I’m sorry, but when did uncorrected findings suddenly become interpretable? I blame the reviewers here over the authors- they should have known better. The MPFC did not survive correction and hence should not be included in anything other than a explicitly stated as such “exploratory analysis”. In fact it’s totally unclear from the methods section of this paper how these findings where at all discovered: did the authors first examine the uncorrected maps and then re-analyze them using the FWE correction? Or did they reduce their threshold in an exploratory post-hoc fashion? These things make a difference and I’m appalled that the reviewers let the article go to print as it is, when figure 1 and the discussion clearly give the non-fMRI savy reader the impression that a main finding of this study is MPFC activation during meditation. Can we please all agree to stop reporting uncorrected p-values?

I will give the authors this much; the descriptions of practice, and the theoretical guideposts are all quite coherent and well put-together. I found their discussion of possible mechanisms of DMN alteration in meditation to be intriguing, even if I do not agree with their conclusion. Still, it pains me to see a paper with so much potential fail to address the pitfalls in meditation research that should now be well known. Indeed the authors themselves make much ado about how difficult proper controls are, yet seem somehow oblivious to the poorly controlled design they here report. This leads me to my own reinterpretation of their data.

A new default mode, or confused controls?

Brewer et al (2011) report that, when using a verbally guided meditation instruction with meditation naive-controls and experienced practitioners, greater activations in PCC, temporal regions, and for loving-kindness, amygdala are found. Given strong evidence by colleagues Christian Jensen et al (2011) that these kinds of contrasts better represent differences in attentional effort than any mechanism inherent to meditation, I can’t help but wonder if what were seeing here is simply some controls trying to follow esoteric instructions and getting confused in the process. Consider the instruction for the choiceless awareness condition:

“Please pay attention to whatever comes into your awareness, whether it is a thought, emotion, or body sensation. Just follow it until something else comes into your awareness, not trying to hold onto it or change it in any way. When something else comes into your awareness, just pay attention to it until the next thing comes along”

Given that in most contemplative traditions, choiceless awareness techniques are typically late-level advanced practices, in which the very concept of grasping to a stimulus is distinctly altered and laden with an often spiritual meaning, it seems obvious to me that such an instruction constitutes and excellent mindwandering inducement for naive-controls. Do you meditate? I do a little, and yet I find these instructions extremely difficult to follow without essentially sending my mind in a thousand directions. Am I doing this correctly?  When should I shift? Is this a thought or am I just feeling hungry? These things constitute mind-wandering but for the controls, I would argue they constitute following the instructions. The point is that you simply can’t make meaningful conclusions about the neural mechanisms involved in mindfulness from these kinds of instructions.

Finally, let’s examine the functional-connectivity analysis. To be honest, there isn’t a whole lot to report here; the functional connectivity during meditation is perhaps confounded by the same issues I list above, which seems to me a probable cause for the diverse spread of regions reported between controls and meditators. I did find this bit to be interesting:

“Using the mPFC as the seed region, we found increased connectivity with the fusiform gyrus, inferior temporal and parahippocampal gyri, and left posterior insula (among other regions) in meditators relative to controls during meditation (Fig. 3, Fig. S1H, and Table S3). A subset of those regions showed the same relatively increased connectivity in meditators during the baseline period as well (Fig. S1G and Table1)

I found it interesting that the meditation conditions appear to co-activate MPFC and insula, and would love to see this finding replicated in properly controlled design. I also have a nagging wonder as to why the authors didn’t bother to conduct a second-level covariance analysis of their findings with the self-reported mind-wandering scores. If these findings accurately reflect meditation-induced alterations in the DMN, or as the authors more brazenly suggest “a entirely new default network”, wouldn’t we expect their PCC modulations to be predicted by individual variability in mind-wandering self-reports? Of course, we could open the whole can of worms that is “what does it mean when you ask participants if they ‘experienced mind wandering” but I’ll leave that for a future review. At least the authors throw a bone to neurophenomenology, suggesting in the discussion that future work utilize first-person methodology. Indeed.

Last, it occurs to me that the primary finding, of increased DLPFC and ACC in meditation>Controls, also fits well with my intepretation that this design is confounded by demand characteristics. If you take a naive subject and put them in the scanner with these instructions, I’ve argued that their probably going to do something a whole lot like mind-wandering. On the other hand, an experienced practitioner has a whole lot of implicit pressure on them to live up to their tradition. They know what they are their for, and hence they know that they should be doing their thing with as much effort as possible. So what does the contrast meditation>naive really give us? It gives us mind-wandering in the naive group, and increased attentional effort in the practitioner group. We can’t conclude anything from this design regarding mechanisms intrinsic to mindfulness; I predict that if you constructed a similar setting with any kind of dedicated specialist, and gave instructions like “think about your profession, what it means to you, remember a time you did really well” you would see the exact same kind of results. You just can’t compare the uncomparable.

Disclaimer: as usual, I review in the name of science, and thank the authors whole-heartily for the great effort and attention to detail that goes into these projects.  Also it’s worth mentioning that my own research focuses on many of these exact issues in mental training research, and hence i’m probably a bit biased in what I view as important issues.

“We are not interested in the logic itself, nor will we argue for replacing the .05 alpha with another level of alpha, but at this point in our discussion we only wish to emphasize that dichotomous significance testing has no ontological basis. That is, we want to underscore that, surely, God loves the .06 nearly as much as the .05. Can there be any doubt that God views the strength of evidence for or against the null as a fairly continuous function of the magnitude of p?”

Rosnow, R.L. & Rosenthal, R. (1989). Statistical procedures and the justification of knowledge in psychological science. American Psychologist, 44, 1276-1284.

This colorful quote came to mind while discussing significance testing procedures with colleagues over lunch. In Cognitive Neuroscience, with it’s enormous boon of obfuscated data, it seems we are so often met with these kinds of seemingly absurd, yet important statistical decisions. Should one correct p-values over the lifetime, as often suggested by our resident methodology expert? I love this suggestion; imagine an academia where the fossilized experts (no offense experts) are tossed aside for the newest and greenest researchers whose pool of p-values remains untapped!

Really though, just how many a priori anatomical hypothesis should one have sealed up in envelopes? As one colleague joked, it seems advantageous to keep a drawer full of wild speculations sealed away in case one’s whole-brain analysis fails to yield results. Of course we must observe and follow best scientific and statistical procedures to their maximum, but in truth a researcher often finds themselves at these obscure impasses, thousands of dollars in scanning funding spent, trying to decide whether or not they predicted a given region’s involvement. In these circumstances, it has even been argued that there is a certain ethical need to explore one’s data and not merely throw away all non-hypothesis fitting findings. While I do not support this claim, I believe it is worth considering. And further, I believe that a vast majority of the field, from the top institutions to the most obscure, often dip into these murky ethical realms.

This is one area I hope “data-driven” science, as in the Human Genome and Human Connectome projects, can succeed. It also points to a desperate need for publishing reform; surely what matters is not how many blobs fall on one side of an arbitrary distinction, but rather a full and accurate depiction of one’s data and it’s implications. In a perfect world, we would not need to obscure the truth hidden in these massive datasets while we hunt for sufficiently low p-values.

Rather we should publish a clear record, showing exactly what was done, what correlated with what, and also where significance and non-significance lie. Perhaps we might one day dream of combing through such datasets, actually explaining what drove the .06′s vs the .05′s. For now however, we must be careful not to look at our uncorrected statistical maps; for that way surely voodoo lie! And that is perhaps the greatest puzzle of all; two datasets, all things being equal. In one case the researcher writes down on paper, “blobs A, B, and C I shall see” and conducts significant ROI analyses on these regions. In the other he first examines the uncorrected map, notices blobs A, B, and C, and then conducts a region of interest analysis. In both cases, the results and data are the same. And yet one is classic statistical voodoo- double dipping- and the other perfectly valid hypothesis testing. It seems thus that our truth criterion lay not only with our statistics, but also in some way, in the epistemological ether.

Of course, it’s really more of a pragmatic distinction than an ontological one. The voodoo distinction serves not to delineate true from false results but rather to discourage researchers from engaging in risky practices that could inflate the risk of false-positives. All-in-all, I agree with Dorothy Bishop: we need to stop chasing the novel, typically spurious and begin to share and investigate our data in ways that create lasting, informative truths. The brain is simply too complex and expensive an object of study to let these practices build into an inevitable file-drawer of doom. It infuriates me how frustratingly obtuse many published studies are, even in top journals, regarding the precise methods and analysis that went into the paper. Wouldn’t we all rather share our data, and help explain it cohesively? I dread the coming collision between the undoubtably monolithic iceberg of unpublished negative findings, spurious positive findings, and our most trusted brain mapping paradigms.

I’ve been crazy busy the past year, primarily mastering Matlab and fMRI statistics. It’s been  a good run and I’ve both collected and analyzed a ton of data. In just a little over a year, I’ve completed a longitudinal study of meditation, including two event-related paradigms, resting-state, DTI, high-resolution T1 anatomical data, and a bevy of physiological and behavioral data. On top of that, I collected a similarly broad dataset for my Varela-award study “The Neurophenomenology of Mindfulness: Meta-cognitive Awareness in Adept Contemplatives”. Finally I’ve written and published a first attempt at a broad-based model of social-cognitive action control, the “Pre-loading Model”.

It’s been a pretty intense year, but I’m excited to write-up and publish my results and you can be sure that I’ll be sharing them here first. I look forward to discussing some of the more intriguing bits, and am also excited by the specter of what looks like a fascinating post-doctoral project.

When I look back I can’t believe everything I’ve learned in the year gone. It feels like every inch of my gray matter has been packed with vital information regarding BOLD timeseries and their statistical analysis. Finally sitting down to write something other than code feels great; I can’t believe the way my perspectives and research interests have changed in absorbing all this new knowledge.

As I move from analyzing my data to writing it up, I hope to return to regular blogging, as I believe this is the best place to vent and develop my ideas, in a living and shared format. I’m not sure exactly what direction i’ll take Neuroconscience in the months to come, but I hope to be able to have fun sharing my ideas and experiences with the community.

It’s not that i’m not sympathetic to the idea we spend too much time in front of a screen. It’s levying the weight of that argument around some BS neurobabble. The brain, and it’s astounding capacity for change, need not enter this debate in an alarmist fashion. If we want to discuss how our society spends its time, let’s not cling onto poorly understood scientific phenomena to do so. Let’s talk about radical capitalism and the 40 hour work week. I spend 38+ hours a week online, programming, and generally living on a computer as do many contemporary workers. Talking about the brain won’t change that- but it will sell books. And I think it puts a clear perspective on the 3-4 hours a week I might spend playing games. The problem isn’t brains, games, or anything in between. Anyone who tries to tell you otherwise wants to make a fast buck off you.

Susan is hoping to use her high-standing to kick off some kind of alarmist movement akin to the global warming debate. She’s even been so cheeky as to give her cause a similar sounding name “Mindchange”, so that her would be activists might have some nice banner around which to rally. Plus, it makes for some pretty obvious best-selling book titles. Maybe even a series of self-help audiobooks filled to the brim with half-truisms, folksy inspiration, and plenty of badly misunderstood science.

And that’s just the problem. Beneath the seemingly innocent wish to make a legacy and buck for herself, Susan has denigrated one big problem (global warming), disrespected the science, and mis-educated her audience. Not only that but I believe she’s obscuring the real problem. Call me radical, but if we don’t start to take serious the cultural-systemic problems that threaten our world, we’re fucked. By capitalizing on sensation, the Baroness has obscured a legitimate debate about the way we spend our lives.

Here’s the abstract, via Evan Thompson

“How the human brain goes virtual: distinct cortical regions of the person processing-network are involved in self-identification with virtual avatars.”

Cerebral Cortex: Shanti Ganesh, Hein T. van Schie, Floris P. de Lange, Evan Thompson, and Daniel H.J. Wigboldus

“We applied functional neuroimaging to 22 long-term online gamers and 21 non-gaming controls, while they rated personality traits of self, avatar and familiar others. Strikingly, neuroimaging data revealed greater avatar-referential cortical activity in the left inferior parietal lobe, a region associated with self-identification from a third-person perspective. The magnitude of this brain activity correlated positively with the propensity to incorporate external body enhancements into one’s bodily identity. Avatar-referencing furthermore recruited greater activity in the rostral anterior cingulate gyrus, suggesting relatively greater emotional self-involvement with one’s avatar. Post-scanning behavioral data revealed superior recognition memory for avatar relative to others. Interestingly, memory for avatar positively co-varied with play duration.”

I’ll admit, I expected the usual “self x vs other x produces greater MPFC activity”. These findings are a nice extension to similiar work by Schilbach et al. I find it particularly interesting that the avatar-related activity correlated with a tendency to couple with external tools; a bit of an Andy Clark-esque vibe there. I look forward to reading the full article (and watching the media go nuts)!