Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3389/fninf.2014.00066 | FRONTIERS IN NEUROINFORMATICS |
Keywords | Field | DocType |
human connectome,resting-state,integrative regions,information theory,multivariate mutual information,complexity measures | Data mining,Predictability,Structure and function,Computer science,Connectome,Resting state fMRI,Brain activity and meditation,Multivariate mutual information,Human brain,Human Connectome | Journal |
Volume | ISSN | Citations |
8 | 1662-5196 | 6 |
PageRank | References | Authors |
0.45 | 14 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Artemy Kolchinsky | 1 | 71 | 8.85 |
| Martijn P van den Heuvel | 2 | 28 | 1.57 |
| Alessandra Griffa | 3 | 97 | 4.83 |
| P. Hagmann | 4 | 511 | 35.38 |
| Luis Mateus Rocha | 5 | 333 | 36.91 |
| Olaf Sporns | 6 | 2633 | 167.36 |
| Joaquín Goñi | 7 | 37 | 4.64 |