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Cohesive Network Reconfiguration Accompanies Extended Training

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Journal Article - Open Access

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University of Pennsylvania Philadelphia United States

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Human behavior is supported by flexible neurophysiological processes that enable the finescalemanipulation of information across distributed neural circuits. Yet, approaches for understandingthe dynamics of these circuit interactions have been limited. One promising avenue for quantifyingand describing these dynamics lies in multilayer network models. Here, networks are composed ofnodes which represent brain regions and time-dependent edges which represent statistical similaritiesin activity time series. We use this approach to examine functional connectivity measured by noninvasiveneuroimaging techniques. These multilayer network models facilitate the examination ofchanges in the pattern of statistical interactions between large-scale brain regions that might facilitatebehavior. In this study, we define and exercise two novel measures of network reconfiguration, anddemonstrate their utility in neuroimaging data acquired as healthy adult human subjects learn a newmotor skill. In particular, we identify putative functional modules in multilayer networks and characterizethe degree to which nodes switch between modules.

Subject Categories:

  • Anatomy and Physiology
  • Medicine and Medical Research
  • Psychology

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