Forthcoming: this is your brain on WoW
July 26th, 2011 § Leave a Comment
Thanks to philosopher and cognitive scientist Evan Thompson for sharing a project that was accepted today in Cerebral Cortex. I’m sure we can expect to see this one get reported all over as soon as the actual article is released (i’m looking at you Wired).
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)!
New Meditation Study in Neuroimage: “Meditation training increases brain efficiency in an attention task”
July 11th, 2011 § 7 Comments
Just a quick post to give my review of the latest addition to imaging and mindfulness research. A new article by Kozasa et al, slated to appear in Neuroimage, investigates the neural correlates of attention processing in a standard color-word stroop task. A quick overview of the article reveals it is all quite standard; two groups matched for age, gender, and years of education are administered a standard RT-based (i.e. speeded) fMRI paradigm. One group has an average of 9 years “meditation experience” which is described as “a variety of OM (open monitoring) or FA (focused attention) practices such as “zazen”, mantra meditation, mindfulness of breathing, among others”. We’ll delve into why this description should give us pause for thought in a moment, for now let’s look at the results.
Results from incon > con, non-meditators vs meditators
In a nutshell, the authors find that meditation-practitioners show faster reaction times with reduced BOLD-signal for the incongruent (compared to congruent and neutral) condition only. The regions found to be more active for non-meditators compared to meditators are the (right) “lentiform nucleus, medial frontal gyrus, and pre-central gyrus” . As this is not accompanied by any difference in accuracy, the authors interpret the finding as demonstrating that “meditators may have maintained the focus in naming the colour with less interference of reading the word and consequently have to exert less effort to monitor the conflict and less adjustment in the motor control of the impulses to choose the correct colour button.” The authors in the conclusion review related findings and mention that differences in age could have contributed to the effect.
So, what are we to make of these findings? As is my usual style, I’ll give a bulleted review of the problems that immediately stand out, and then some explanation afterwards. I’ll preface my critique by thanking the authors for their hard work; my comments are intended only for the good of our research community.
The good:
- Sensible findings; increases in reaction time and decreases in bold are demonstrated in areas previously implicated in meditation research
- Solid, easy to understand behavioral paradigm
- Relatively strong main findings ( P< .0001)
- A simple replication. We like replications!
- Appears to report uncorrected p-values
- Study claims to “match samples for age” yet no statistical test demonstrating no difference is shown. Qualitatively, the ages seem different enough to be cause for worry (77.8% vs 65% college graduates). Always be suspicious when a test is not given!
- Extremely sparse description of style of practice, no estimate of daily practice hours given.
- Reaction-time based task with no active control
I’ll preface my conclusion with something Sara Lazar, a meditation researcher and neuroimaging expert at the Harvard MGH told me last summer; we need to stop going for the “low hanging fruit of meditation research”. There are now over 20 published cross-sectional reaction-time based fMRI studies of “meditators” and “non-meditators”. Compare that to the incredibly sparse number of longitudinal, active controlled studies, and it is clear that we need to stop replicating these findings and start determining what they actually tell us. Why do we need to active control our meditation studies? For one thing, we know that reaction-time based tests are heavily based by the amount of effort one expends on the task. Effort is in turn influenced by task-demands (e.g. how you treat your participants, expectations surrounding the experiment). To give one in-press example, my colleagues Christian Gaden Jensen at the Copenhagen Neurobiology Research recently conducted a study demonstrating just how strong this confounding effect can be.
To briefly summarize, Christian recruited over 150 people for randomization to four experimental groups: mindfulness-based stress reduction (MBSR), non-mindfulness stress reduction (NMSR), wait-listed controls, and financially-motivated wait-listed controls. This last group is the truly interesting one; they were told that if they had top performance on the experimental tasks (a battery of classical reaction-time based and unspeeded perceptual threshold tasks) they’d receive a reward of approximately 100$. When Christian analyzed the data, he found that the financial incentive eliminated all reaction-time based differences between the MBSR, NMSR, and financially motivated groups! It’s important to note that this study, fully randomized and longitudinal, showed something not reflected in the bulk of published studies: that meditation may actually train more basic perceptual sensitivities rather than top-down control. This is exactly why we need to stop pursuing the low-hanging fruit of uncontrolled experimental design; it’s not telling us anything new! Meditation research is no longer exploratory.
In addition to these issues, there is another issue a bit more specific to meditation research. That is the totally sparse description of the practice- less than one sentence total, with no quantitative data! In this study we are not even told what the daily practice actually consists of, or its quality or length. These practitioners report an average of 8 years practice, yet that could be 1 hour per week of mantra meditation or 12 hours a week of non-dual zazen! These are not identical processes and our lack of knowledge for this sample severely limits our ability to assess the meaning of these findings. For the past two years (and probably longer) of the Mind & Life Summer Research Institute, Richard Davidson and others have repeatedly stated that we must move beyond studying meditation as “a loose practice of FA and OM practices including x, y, z, & and other things”. Willoughby Britton suggested at a panel discussion that all meditation papers need to have at least one contemplative scholar on them or risk rejection. It’s clear that this study was most likely not reviewed by anyone with any serious academic background in meditation research.
My supervisor Antoine Lutz and his colleague John Dunne, authors of the paper that launched the “FA/OM” distinction, have since stated emphatically that we must go beyond these general labels and start investigating effects of specific meditation practices. To quote John, we need to stop treating meditation like a “black box” if we ever want to understand the actual mechanisms behind it. While I thank the authors of this paper for their earnest contribution, we need to take this moment to be seriously skeptical. We can only start to understand processes like meditation from a scientific point of view if we are willing to hold them to the highest of scientific standards. It’s time for us to start opening the black box and looking inside.
Intrinsic correlations between Salience, Primary Sensory, and Default Mode Networks following MBSR
February 22nd, 2011 § 2 Comments
Going through my RSS backlog today, I was excited to see Kilpatrick et al.’s “Impact of Mindfulness-Based Stress Reduction Training on Intrinsic Brain Connectivity” appear in this week’s early view Neuroimage. Although I try to keep my own work focused on primary research in cognition and connectivity, mindfulness-training (MT) is a central part of my research. Additionally, there are few published findings on intrinsic connectivity in this area. Previous research has mainly focused on between-group differences in anatomical structure (gray and white matter for example) and task-related activity. A few more recent studies have gone as far as to randomize participants into wait-listed control and MT groups.
While these studies are interesting, they are of course limited in their scope by several factors. My supervisor Antoine Lutz emphasizes that in addition to our active-controlled research here in Århus, his group at Wisconsin-Madison and others are actively preparing such datasets. Active controls are simply ‘mock’ interventions (or real ones) designed to control for every possible aspect of being involved in an intervention (placebo, community, motivation) in order to isolate the variables specific to that treatment (in this case, meditation, but not sitting, breathing, or feeling special). Active controls are important as there is a great deal of research demonstrating that cognition itself is susceptible to placebo-like motivational effects. All and all, I’ve seen several active-controlled, cognitive-behavioral studies in review that suggest we should be strongly skeptical of any non-active controlled findings. While I can’t discuss these in detail, I will mention some of these issues in my review of the neuroimage manuscript. It suffices to say however, that if you are working on a passive-controlled study in this area, you had better get it out fast as you can expect reviewers to be greatly tightening their expectations in the coming months, as more and more rigorous papers appear. As Sara Lazar put it during my visit to her lab last summer “the low-hanging fruit of MBSR brain research are rapidly vanishing”. Overall this is a good thing for the community and we’ll see why in a moment.
Now let us turn to the paper at hand. Kilpatrick et al start with a standard summary of MBSR and rsfMRI research, focusing on findings indicating MBSR trains focused attention, sensory introspection/interception and perception. They briefly review now well-established findings indicating that rsfMRI is sensitive to training related changes, including studies that demonstrate the sensitivity of the resting state to conditions such as fatigue, eyes-open vs eyes-closed, and recent sleep. This is all pretty well and good, but I think it’s a bit odd when we see just how they collect their data.
Briefly, they recruited 32 healthy adults for randomization to MBSR and waitlist controls. Controls then complete the Mindfulness Attention Awareness Scale (MAAS) and receive 8 weeks of diary-logged standard MBSR training. After training, participants are recalled for the rsfMRI scan. An important detail here is that participants are not scanned before and after training, rendering the fMRI portion of the experiment closer to a cross-section than true longitudinal design. At the time of scan, the researchers then give two ‘task-free states’, with and without auditory white noise. The authors indicate that the noise condition is included “to enable new analysis methods not conducted here”, presumably to average out scanner-noise related affects. They later indicate no differences between the two conditions, which causes me to ask how much here is meditation vs focusing-on-scanner-noise specific. Finally, they administer the ‘task free’ states with a slight twist:
“”During this baseline scan of about 5 min, we would like you to again stay as still as possible and be mindfully aware of your surroundings. Please keep your eyes closed during this procedure. Continue to be mindfully aware of whatever you notice in your surroundings and your own sensations. Mindful awareness means that you pay attention to your present moment experience, in this case the changing sounds of the scanner/changing background sounds played through the headphones, and to bring interest and curiosity to how you are responding to them.”
While the manipulation makes sense given the experimenter’s hypothesis concerning sensory processing, an ongoing controversy in resting-state research is just what it is that constitutes ‘rest’. Research here suggests that functional connectivity is sensitive to task-instructions and variations in visual stimulation, and many complain about the lack of specificity within different rest conditions. Kilpatrick et al’s manipulation makes sense given that what they really want to see is meditation-related alterations, but it’s a dangerous leap without first establishing the relationship between ‘true rest’ and their ‘auditory meditation’ condition. Research on the impact of scanner-noise indicates some degree of noise-related nuisance effects, and also some functionally significant effects. If you’ve never been in an MR experiment, the scanner is LOUD. During my first scan I actually started feeling claustrophobic due to the oppressive machine-gun like noise of the gradient coil. Anyway, it’s really troubling that Kilpatrick et al don’t include a totally task-free set for comparison, and I’m hesitant to call this a resting-state finding without further clarification.
The study is extremely interesting, but it’s important to note it’s limitations:
- Lack of active control- groups are not controlled for motivation.
- No pre/post scan.
- Novel resting state without comparison condition.
- Findings are discussed as ‘training related’ without report of correlation with reported practice hours.
- Anti-correlations reported with global-signal nuisance regression. No discussion of possible regression related inducement (see edit).
- Discussion of findings is unclear; reported as greater DMN x Auditory correlation, but the independent component includes large portions of the salience network.
Ultimately they identify a “auditory/salience” independent component network (ICN) (primary auditory, STG, posterior Insula, ACC, and lateral frontal cortex) and then conduct seed-regression analyses of the network with areas of the DMN and Dorsal Attention Network (DAN). I find it highly strange that they pick up a network that seems to conflate primary sensory and salience regions, as do the researchers who state “Therefore, the ICN was labeled as “auditory/salience”. It is unclear why the components split differently in our sample, perhaps the instructions that brought attention to auditory input altered the covariance structure somewhat.” Given the lack of motivational control in the study, the issues in this study begin to pile onto one another and I am not sure what we can really conclude. They further find that the MBSR group demonstrates greater “auditory/salience x DMN connectivity”, “greater visual and auditory functional connectivity” (see image below). 
They also report several increased anti-correlations, between the aud/sal network, dMPFC and visual regions. I find this to be an extremely tantalizing finding as it would reflect a decrease in processing automaticity amongst the SAL, CEN, and DMN networks. There are some serious problems with these kinds of analysis that the authors don’t address, and so we again must reserve any strong conclusions. Here is what Kilpatrick et al conclude:
“The current findings extend the results of prior studies that showed meditation-related changes in specific brain regions active during attention and sensory processing by providing evidence that MBSR trained compared to untrained subjects, during a focused attention instruction, have increased connectivity within sensory networks and between regions associated with attentional processes and those in the attended sensory cortex. In addition they show greater differentiation between regions associated with attentional processes and the unattended sensory cortex as well as greater differentiation between attended and unattended sensory networks”
As is typical, the list of findings is quite long and I won’t bother re-stating it all here. Given the resting instructions it seems clear that the freshly post-MBSR participants are likely to have engaged a pretty dedicated set of cognitive operations during the scan. Yet it’s totally unclear what the control group would do given these contemplative instructions. Presumably they’d just lie in the scanner and try not to tune out the noise- but you can see here how it’s not clear that these conditions are really that comparable without having some idea of what’s going on. In essence what you (might) have here is one group actually doing something (meditation) and the other group not doing much at all. Ideally you want to see how training impacts the underlying process in a comparable way. Motivation has been repeatedly linked to BOLD signal intensity and in this case, it could very well be that these findings are simple artifacts of motivation to perform. If one group is actually practicing mindfulness and the other isn’t, you have not isolated the variable of interest. The authors could have somewhat alleviated this by including data from the additional pain task (“not reported here”) and/or at least giving us a correlation of the findings with the MAAS scale. I emphasize that I do find the findings of this paper interesting- they map extremely well onto my own hypotheses about how RSNs interact with mindfulness training, but that we must interpret them with caution.
Overall I think this was a project with a strong theoretical motivation and some very interesting ideas. One problem with looking at state-mindfulness in the scanner is the cramped, noisy environment. I think Kilpatrick et al had a great idea in their attempt to use the noise itself as a manipulation. Further, the findings make a good deal of sense. Still, given the above limitation, it’s important to be really careful with our conclusions. At best, this study warrants an extremely rigorous follow-up, and I wish neuroimage had published it with a bit more information, such as the status of any rest-MAAS correlations. Anyway, this post has gotten quite long and I think I’d best get back to work- for my next post I think I’ll go into more detail about some of the issues confront resting state (what is “rest”?) and mindfulness (role of active controls for community, motivation, and placebo effects) and what they mean for resting-state research.
edit: just realized I never explained limitation #5. See my “beautiful noise” slides (previous post) regarding the controversy of global signal regression and anti-correlation. Simply put, there is somewhat convincing evidence that this procedure (designed to eliminate low-frequency nuisance co-variates) may actually mathematically induce anti-correlations where none exist, probably due to regression to the mean. While it’s not a slam-dunk (see response by Fox et al), it’s an extremely controversial area and all anti-correlative findings should be interpreted in light of this possibility.
If you like this post please let me know in the comments! If I can get away with rambling about this kind of stuff, I’ll do so more frequently.
