Multi-scale integration and predictability in resting state brain activity

• Autor(a) principal: Kolchinsky, Artemy
• Data de Publicação: 2014
• Outros Autores: van den Heuvel, Martijn P., Griffa, Alessandra, Hagmann, Patric, Rocha, Luis M., Sporns, Olaf, Goñi, Joaquín
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.7/382
• topic: human connectome; resting-state; integrative regions; information theory; multivariate mutual information; complexity measures

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.