Title |
Multi-scale integration and predictability in resting state brain activity
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Published in |
Frontiers in Neuroinformatics, July 2014
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DOI | 10.3389/fninf.2014.00066 |
Pubmed ID | |
Authors |
Artemy Kolchinsky, Martijn P. van den Heuvel, Alessandra Griffa, Patric Hagmann, Luis M. Rocha, Olaf Sporns, Joaquín Goñi |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 29% |
Belgium | 2 | 12% |
United Kingdom | 1 | 6% |
Finland | 1 | 6% |
Australia | 1 | 6% |
Sweden | 1 | 6% |
Solomon Islands | 1 | 6% |
Turkey | 1 | 6% |
Unknown | 4 | 24% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 9 | 53% |
Scientists | 6 | 35% |
Science communicators (journalists, bloggers, editors) | 2 | 12% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 5% |
France | 2 | 2% |
Netherlands | 2 | 2% |
Chile | 1 | 1% |
Italy | 1 | 1% |
Belgium | 1 | 1% |
Canada | 1 | 1% |
Japan | 1 | 1% |
Spain | 1 | 1% |
Other | 0 | 0% |
Unknown | 84 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 34 | 34% |
Researcher | 21 | 21% |
Student > Master | 12 | 12% |
Professor > Associate Professor | 6 | 6% |
Student > Doctoral Student | 5 | 5% |
Other | 12 | 12% |
Unknown | 9 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 21 | 21% |
Psychology | 19 | 19% |
Medicine and Dentistry | 12 | 12% |
Computer Science | 11 | 11% |
Engineering | 10 | 10% |
Other | 14 | 14% |
Unknown | 12 | 12% |