Researching Neuroplasticity, Cognitive Neuroscience, and Cognitive Science

Tag: research methods

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


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.


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).

A defense of vegetarian fMRI (1/2)

Recently there’s been much ado about a newly published fMRI study of empathetic responding in vegetarians, vegans, and omnivores. The study isn’t perfect, which the authors admit, but I find it interesting and relatively informative for an fMRI paper. The Neurocritic doesn’t, rather he raises some seemingly serious issues with the study. I promised on twitter I’d defend my claim that the study is good (and that neurocritic could do better). But first, a motivated ramble to distract and confuse you.

As many of you might realize, neuroscience could be said to be going through something like puberty. While the public remains infatuated with every poorly worded research report, researchers within the neurosciences have to view brain mapping through an increasingly skeptical lens. This is a good thing: science progresses through the introduction and use of new technologies and the eventual skeptical refinement of their products.

And certainly there is plenty of examples shoddy neuroscience out there, whether it’s reports of voodoo correlations or inconsistencies between standard fMRI analyses packages. Properly executed, attention to these issues and a healthy skepticism of the methods will ultimately result in a refined science. Yet we must also be careful to apply the balm of skepticism in a refined manner: neuroscientists are people to, and we work in an increasingly competitive field where there are few well-defined standards and even less clarity.

Take an example from my lab that happened just today.  We’re currently analyzing some results from a social cognition experiment my colleague Kristian Tylen and I conducted last year. Like many fMRI results, our hypotheses (which were admitable a bit vague when we made them) were not exactly supported by our findings. Rather we ended up with a scattered series of blobs that appeared to mostly center on early visual areas. This is obviously boring and unpublishable, and after some time we decided to do a small volume correction on some areas we’d discussed in a published paper. This finally revealed some interesting findings somewhere around the TPJ, which brings me to the point of this story.

My research has thus far mostly focused on motor and prefrontal regions. We in neuroimaging can often fall victim to what I call ‘blob blind sight’ where we focus so greatly on a single area or handful of areas that we forget there’s’ a wide world of cortex out there. Imagine my surprise when I tried to get clear about whether our finding was situated in exactly the pSTS, TPJ, or nearby inferior parietal lobule (IPL) only to discover that these three areas are nearly indistinguishable from one another anatomically.

All of these regions are involved in different aspects of social cognition, and across the literature there are no clear anatomical differentiation between them. In many cases, researchers will just lump them together as pSTS/TPJ, regardless of the fact that a great deal of research has gone on explicitly differentiating them. Now what does one do with a blob that lands somewhere in the middle, overlapping all three? More specifically, imagine the case where your activation foci lands smack dab in the middle, or a few voxels to the left. Is it TPJ? Or IPL? Or is it really the conjunction of all three, and if so, how does one make sense of that given the wide array of functions and connectivity patterns for these areas. IPL is a part of the default mode, whereas TPJ and pSTS are not. It’s really quite a mess, and the answer you choose will likely depend upon the interpretation you give, given the vast variety of functions allocated to these three regions.

The point of all this, which begins to lead to my critique of TNC critique, is that it is not a simple matter of putting ones foot down and claiming that the lack of an expected activation or the presence of an unexpected one is damning or indicative of bad science. It’s an inherent problem in a field where hundreds of papers are published monthly with massive tables of activation foci. To say that a study has gone awry because they don’t report your favorite area misses the point. What’s more important is to evaluate the methods and explain the totality of the findings reported.

So that’s one huge issue confronting most researchers. Although there are some open source ‘foci databases’ out there, they are underused and hard to rely on. One can of course try to pinpoint the exact area, but in reality the chance that you’ll have such a focused blob is pretty unlikely. Rather, researchers have to rely on extra-scanner measures and common sense to make any kind of interesting theoretical inferences from fMRI. This post was meant to be a response to The Neurocritic, who took issue with my taking issue of his taking issue with a certain vegetarian fmri study… but I’m already an hour late coming home from work and I’m afraid I’ve failed to deliver. I did take the time this afternoon to go thoroughly through both the paper and TNC’s response however, and I think I’ve got a pretty compelling argument. Next time: why the neurocritic is plain wrong ;)


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