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Multi-scale integration and predictability in resting state brain activity.


ABSTRACT: The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales.

SUBMITTER: Kolchinsky A 

PROVIDER: S-EPMC4109611 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Multi-scale integration and predictability in resting state brain activity.

Kolchinsky Artemy A   van den Heuvel Martijn P MP   Griffa Alessandra A   Hagmann Patric P   Rocha Luis M LM   Sporns Olaf O   Goñi Joaquín J  

Frontiers in neuroinformatics 20140724


The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spat  ...[more]

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