Multi-scale integration and predictability in resting state brain activity
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , , , |
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 |
Resumo: | 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. |
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Multi-scale integration and predictability in resting state brain activityhuman connectomeresting-stateintegrative regionsinformation theorymultivariate mutual informationcomplexity measuresThe 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.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.Frontiers Research FoundationARCAKolchinsky, Artemyvan den Heuvel, Martijn P.Griffa, AlessandraHagmann, PatricRocha, Luis M.Sporns, OlafGoñi, Joaquín2015-10-07T15:05:27Z2014-07-242014-07-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.7/382engKolchinsky 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.2014.0006610.3389/fninf.2014.00066info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-11-29T14:34:46Zoai:arca.igc.gulbenkian.pt:10400.7/382Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:11:40.941161Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Multi-scale integration and predictability in resting state brain activity |
title |
Multi-scale integration and predictability in resting state brain activity |
spellingShingle |
Multi-scale integration and predictability in resting state brain activity Kolchinsky, Artemy human connectome resting-state integrative regions information theory multivariate mutual information complexity measures |
title_short |
Multi-scale integration and predictability in resting state brain activity |
title_full |
Multi-scale integration and predictability in resting state brain activity |
title_fullStr |
Multi-scale integration and predictability in resting state brain activity |
title_full_unstemmed |
Multi-scale integration and predictability in resting state brain activity |
title_sort |
Multi-scale integration and predictability in resting state brain activity |
author |
Kolchinsky, Artemy |
author_facet |
Kolchinsky, Artemy van den Heuvel, Martijn P. Griffa, Alessandra Hagmann, Patric Rocha, Luis M. Sporns, Olaf Goñi, Joaquín |
author_role |
author |
author2 |
van den Heuvel, Martijn P. Griffa, Alessandra Hagmann, Patric Rocha, Luis M. Sporns, Olaf Goñi, Joaquín |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
ARCA |
dc.contributor.author.fl_str_mv |
Kolchinsky, Artemy van den Heuvel, Martijn P. Griffa, Alessandra Hagmann, Patric Rocha, Luis M. Sporns, Olaf Goñi, Joaquín |
dc.subject.por.fl_str_mv |
human connectome resting-state integrative regions information theory multivariate mutual information complexity measures |
topic |
human connectome resting-state integrative regions information theory multivariate mutual information complexity measures |
description |
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. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-07-24 2014-07-24T00:00:00Z 2015-10-07T15:05:27Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.7/382 |
url |
http://hdl.handle.net/10400.7/382 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
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 10.3389/fninf.2014.00066 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Frontiers Research Foundation |
publisher.none.fl_str_mv |
Frontiers Research Foundation |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799130572208996352 |