Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.7/382
Título: Multi-scale integration and predictability in resting state brain activity
Autor: Kolchinsky, Artemy
van den Heuvel, Martijn P.
Griffa, Alessandra
Hagmann, Patric
Rocha, Luis M.
Sporns, Olaf
Goñi, Joaquín
Palavras-chave: human connectome
resting-state
integrative regions
information theory
multivariate mutual information
complexity measures
Data: 24-Jul-2014
Editora: Frontiers Research Foundation
Citação: Kolchinsky A, van den Heuvel MP, Griffa A, Hagmann P, Rocha LM, Sporns O and Goñi J (2014) Multi-scale integration and predictability in resting state brain activity. Front. Neuroinform. 8:66. doi: 10.3389/fninf.2014.00066
Resumo: 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.
Peer review: yes
URI: http://hdl.handle.net/10400.7/382
DOI: 10.3389/fninf.2014.00066
Versão do Editor: http://journal.frontiersin.org/article/10.3389/fninf.2014.00066/abstract
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