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Cognitive Regime Shift II - When/why/how the Brain Breaks/CaterinaGratton

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Notes by user Caterina Gratton (Northwestern Univ.) for Cognitive Regime Shift II - When/why/how the Brain Breaks

Post-meeting Reflection

1+ paragraphs on any combination of the following:

  • Presentation highlights
  • Open questions that came up
  • How your perspective changed
  • Impact on your own work
  • e.g. the discussion on [A] that we are having reminds me of [B] conference/[C] initiative/[D] funding call-for-proposal/[E] research group

I started my section with the premise that when we discuss the brain "breaking", we often operationalize this in terms of changes in complex behaviors, and that these behaviors may be subserved by large-scale systems of the brain. Much of the work to date has focused on the typical average structure of these systems. I showed some recent work we've done aimed at moving these analyses to the individual level, and discussed some observations we've made based on this.

(1) I showed that functional network measurements (at rest) can be quite reliable even in single individuals, given enough data

(2) I showed some data demonstrating that functional network measurements are dominated by stable factors including group commonalities and individual features. Task-state and day-to-day variability is also present, but much smaller in scale.

(3) I discussed our characterizations of punctate locations of individual differences in functional networks, showing that these locations are present across repeated recordings, relate to altered function, and individuals cluster based on the forms of variants they exhibit. While these individual differences explain some (gross) behavioral differences, the variance they explain is very small. I left off with a question to the group of why this might be: why do we see relatively stable behavior in the face of some large individual differences in brain organization.

Discussion centered on how we might think about these effects in the context of distributed organization (or not), to what extent these effects can be overcome by functional alignment that does not assume spatial correspondence, and whether manifolds might be a way of modeling variation in brain function that can lead to a similar functional outcome. We also discussed whether behavior has been measured well enough yet, or if we've been too non-specific in our functional assessments.

General meeting reflection: There were some interesting discussions of multiple different scales and ways of thinking about the brain. I would have liked to have seen a little more cross-talk integration, and/or thoughts about practical directions on which to move forward. How can we better unite models with data? What are the right types of data to collect and theories to test?

Reference material notes

Some examples:

  • Here is [A] database on [B] that I pull data from to do [C] analysis that might be of interest to this group (insert link).
  • Here is a free tool for calculating [ABC] (insert link)
  • This painting/sculpture/forms of artwork is emblematic to our discussion on [X]!
  • Schwartz et al. 2017 offers a review on [ABC] migration as relate to climatic factors (add the reference as well).
  • Warren et al. 2014 discusses a case where network models of the brain may help to provide information about behavioral disruptions after brain damage.
  • Gratton et al. 2018 reviews aspects of the forms of variation available in functional MRI measurements, which may constrain which types of questions different fMRI measures are best suited to addressing.

Reference Materials

Title Author name Source name Year Citation count From Scopus. Refreshed every 5 days. Page views Related file
Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation Caterina Gratton, Timothy O. Laumann, Ashley N. Nielsen, Deanna J. Greene, Evan M. Gordon, Adrian W. Gilmore, Steven M. Nelson, Rebecca S. Coalson, Abraham Z. Snyder, Bradley L. Schlaggar, Nico U.F. Dosenbach, Steven E. Petersen Neuron 2018 0 4 Download
Network measures predict neuropsychological outcome after brain injury David E. Warren, Jonathan D. Power, Joel Bruss, Natalie L. Denburg, Eric J. Waldron, Haoxin Sun, Steven E. Petersen, Daniel Tranel Proceedings of the National Academy of Sciences of the United States of America 2014 0 3 Download