Multi-scale Integration and Predictability in Resting State Brain Activity

Frontiers in Neuroinformatics

Patric eHagmann¹  Alessandra eGriffa¹  Olaf eSporns²  Joaquin eGoñi²  Artemy eKolchinsky³  Luis M. eRocha³  Martijn P. eVan Den Heuvel⁴ 

[1] Ecole Polytechnique Fédérale de Lausanne;Indiana University;Instituto Gulbenkian de Ciência;University Medical Center;

关键词: resting-state;    human connectome;    complexity measures;    integrative regions;    information-theory;    multivariate mutual information;   

DOI  :  10.3389/fninf.2014.00066

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown