Danielle S. Bassett, Nicholas F. Wymbs, M. Puck Rombach, Mason A. Porter, Peter J. Mucha, Scott T. Grafton
As a person learns a new skill, distinct synapses, brain regions, and networks are engaged and change over time. To better understand the dynamic processes that integrate information across a set of regions to enable the emergence of novel behaviour, we measure brain activity during motor sequencing and characterise network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities of brain regions that control learning, we find that the complex reconfiguration patterns of local communities can be described parsimoniously by the combined presence of a relatively stiff core of primary sensorimotor and visual regions whose connectivity changes little in time and a flexible periphery of multimodal association regions whose connectivity changes frequently. The separation between core and periphery changes with the duration of task practice and, importantly, is a good predictor of individual differences in learning success. This temporally defined core-periphery organisation corresponds with notions of core-periphery organisation established previously in social networks: The geometric core of strongly connected regions tends to also be the temporal core of stiff regions. We then show, using hypergraphs, that the core is dominated by the co-evolution of network edges, demonstrating that core regions turn on and off with the same set of partner regions over the slow time scale of learning. Our results demonstrate that an organisational structure - namely, core-periphery structure - provides a fundamental new approach for understanding how separable functional modules are linked. This, in turn, enables the prediction of fundamental capacities, including the production of complex goal-directed behaviour, in humans.
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http://arxiv.org/abs/1210.3555
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