Publication
Multi-scale integration and predictability in resting state brain activity
| dc.contributor.author | Kolchinsky, Artemy | |
| dc.contributor.author | van den Heuvel, Martijn P. | |
| dc.contributor.author | Griffa, Alessandra | |
| dc.contributor.author | Hagmann, Patric | |
| dc.contributor.author | Rocha, Luis M. | |
| dc.contributor.author | Sporns, Olaf | |
| dc.contributor.author | Goñi, Joaquín | |
| dc.date.accessioned | 2015-10-07T15:05:27Z | |
| dc.date.available | 2015-10-07T15:05:27Z | |
| dc.date.issued | 2014-07-24 | |
| dc.description.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. | pt_PT |
| dc.description.sponsorship | Indiana University School of Informatics (NSFIGERT program in Brain-Body- Environment Systems), Netherlands Organization for Scientific Research Grant: (VENI-451-12-001), Brain Center Rudolf Magnus fellowship, Swiss National Science Foundation (Schweizerische Nationalfonds Grant 320030-130090), Intelligence Advanced Research Projects Activity (Open Source Indicators), Indiana University Collaborative Research Grant, Mcdonnell Foundation. | pt_PT |
| dc.identifier | 10.3389/fninf.2014.00066 | |
| dc.identifier.citation | 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 | pt_PT |
| dc.identifier.doi | 10.3389/fninf.2014.00066 | |
| dc.identifier.uri | http://hdl.handle.net/10400.7/382 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Frontiers Research Foundation | pt_PT |
| dc.relation.publisherversion | http://journal.frontiersin.org/article/10.3389/fninf.2014.00066/abstract | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | human connectome | pt_PT |
| dc.subject | resting-state | pt_PT |
| dc.subject | integrative regions | pt_PT |
| dc.subject | information theory | pt_PT |
| dc.subject | multivariate mutual information | pt_PT |
| dc.subject | complexity measures | pt_PT |
| dc.title | Multi-scale integration and predictability in resting state brain activity | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 15 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | Frontiers in Neuroinformatics | pt_PT |
| oaire.citation.volume | 8 | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |