Thursday, September 26, 2013

"Task-Based Core-Periphery Organization of Human Brain Dynamics"

Another of my papers just came out today, though I have to say that putting 12 pieces of supplementary information (8 figures, 3 tables, and supplementary text) as 13 separate files is absolutely ridiculous. It also led to some botching on the part of the journal --- such as in the table numbering in the .pdf versions and a messed-up reference in the caption of a table in the .pdf version --- and we never had the chance to do anything about it because this journal doesn't do page proofs. Sigh... Anyway, here is a combined version of the .pdf files that my postdoc Sang Hoon Lee assembled, and here is the accepted preprint version of the paper.

And now for the details about the paper itself...

Title: Task-Based Core-Periphery Organization of Human Brain Dynamics

Authors: Danielle S. Bassett, Nicholas F. Wymbs, M. Puck Rombach, Mason A. Porter, Peter J. Mucha, and Scott T. Grafton

Abstract: As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal core that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal periphery that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.

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