Neuroconscience

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

Tag: rsfMRI

Intrinsic correlations between Salience, Primary Sensory, and Default Mode Networks following MBSR

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:

  1. Lack of active control- groups are not controlled for motivation.
  2. No pre/post scan.
  3. Novel resting state without comparison condition.
  4. Findings are discussed as ‘training related’ without report of correlation with reported practice hours.
  5. Anti-correlations reported with global-signal nuisance regression. No discussion of possible regression related inducement (see edit).
  6. 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.

Slides from my recent Interacting Minds talk

Switching between executive and default mode networks in posttraumatic stress disorder [excerpts and notes]

From: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2895156/?tool=pubmed

Daniels et al, 2010

We decided to use global scaling because we were not analyzing anticorrelations in this paradigm and because data presented by Fox and colleagues66 and Weissenbacher and coworkers65 indicate that global scaling enhances the detection of system-specific correlations and doubles connection specificity. Weissenbacher and colleagues65 compared different preprocessing approaches in human and simulated data sets and recommend applying global scaling to maximize the specificity of positive resting-state correlations. We used high-pass filtering with a cut-off at 128 seconds to minimize the impact of serial autocorrelations in the fMRI time series that can result from scanner drift.

Very useful methodological clipping!

The control condition was a simple fixation task, requiring attention either to the response instruction or to a line of 5 asterisks in the centre of the screen. We chose this control task to resemble the activation task as closely as possible; it therefore differed considerably from previous resting state analyses because it was relatively short in duration and thus necessitated fast switches between the control condition and the activation task. It also prompted the participants to keep their eyes open and fixated on the stimulus, which has been shown to result in stronger default mode network activations than the closed-eyes condition.60

Good to remember: closed-eyed resting states result in weaker default mode activity.

To ensure frequent switching between an idling state and task-induced activation, we used a block design, presenting the activation task (8 volumes) twice interspersed with the fixation task (4 volumes) within each of 16 imaging runs. Each task was preceded by an instruction block (4 volumes duration), amounting to a total acquisition of 512 volumes per participant. The order of the working memory tasks was counterbalanced between runs and across participants. Full details of this working memory paradigm are provided in the study by Moores and colleagues.6 There were 2 variations of this task in each run concerning the elicited button press response; however, because we were interested in the effects of cognitive effort on default network connectivity, rather than specific effects associated with a particular variation of the task, we combined the response variations to model a single “task” condition for this study. The control condition consisted of periods of viewing either 5 asterisks in the centre of the screen or a notice of which variation of the task would be performed next.

Psychophysiological interaction analyses are designed to measure context-sensitive changes in effective connectivity between one or more brain regions67 by comparing connectivity in one context (in the current study, a working memory updating task) with connectivity during another context (in this case, a fixation condition). We used seed regions in the mPFC and PCC because both these nodes of the default mode network act independently across different cognitive tasks, might subserve different subsystems within the default mode network and have both been associated with alterations in PTSD.8

This paradigm is very interesting. The authors have basically administered a battery of working memory tasks with interspersed rest periods, and carried out ROI inter-correlation, or seed analysis. Using this simple approach, a wide variety of experimenters could investigate task-rest interactions using their existing data sets.

Limitations

The limitations of our results predominantly relate to the PTSD sample studied. To investigate the long-lasting symptoms that accompany a significant reduction of the general level of functioning, we studied alterations in severe, chronic PTSD, which did not allow us to exclude patients taking medications. In addition, the small sample size might have limited the power of our analyses. To avoid multiple testing in a small sample, we only used 2 seed regions for our analyses. Future studies should add a resting state scan without any visual input to allow for comparison of default mode network connectivity during the short control condition and a longer resting state.

The different patterns of connectivity imply significant group differences with task-induced switches (i.e., engaging and disengaging the default mode network and the central-executive network).
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