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

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Teaser from my upcoming submission – Changes of cognitive-affective neural processing following active-controlled mindfulness intervention

As I’ve been dreadfully quiet in the weeks leading up to the submission of my first fMRI paper, I thought I’d give my readers a little tidbit teaser of my (hopefully) forthcoming article. We’re within days of submission and I’ve got high hopes for a positive review. Here is the abstract:

Mindfulness meditation is a set of attention-based, regulatory and self-inquiry training regimes. Although the impact of mindfulness meditation training (MT) on self-regulation is well established, the neural mechanisms supporting such plasticity are poorly understood. MT is thought to act on attention through bottom-up salience and top-down control mechanisms, but until now conflicting evidence from behavioral and neural measures has made it difficult to distinguish the role of these mechanisms. To resolve this question we conducted a fully randomized 6-week longitudinal trial of MT, explicitly controlling for cognitive and treatment effects. We measured behavioral metacognition and whole-brain BOLD signals during an affective Stroop task before and after intervention. Although both groups improved significantly on a response-inhibition task, only the MT group showed reduced affective Stroop conflict. Moreover, the MT group showed greater dorsolateral prefrontal cortex (DLPFC) responses during executive processing, indicating increased recruitment of top-down mechanisms to resolve conflict. Individual differences in MT adherence predicted improvements in response-inhibition and increased recruitment of dorsal anterior cingulate cortex (dACC), medial prefrontal cortex (mPFC), and right anterior insula during negative valence processing, suggesting that rigorous mindfulness practice precedes alterations of bottom-up processes.

And a teaser figure:


Figure 5, Greater levels of meditation practice predict increased dorsolateral prefrontal, right anterior insula, and medial-prefrontal BOLD recruitment during negative > neutral trials. pFWE < 0.05 corrected on cluster level, voxel selection threshold p = 0.001.

Things are fantastic, especially since I’ve moved to London. The ICN is a great place for cognitive neuroscience and I’m learning and doing more than I ever have before. While I prepare this paper, I am simultaneously finishing up a longitudinal VBM analysis of the same data, and beginning to script an eventual 60 subject affective-stroop Dynamic Causal Modeling connectivity study. Everyone here is insanely talented and there is hardly a day that goes by when there isn’t some interesting discussion, a fascinating talk, or an exciting collaboration to be had.

disclaimer: these findings have NOT been peer reviewed and as such should not be believed nor reported as science! They’re just pretty pictures for now.

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).

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