Insights into brain architectures from the homological scaffolds of functional connectivity networks

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Lord, Louis-Davidca
• dc.contributor.author Expert, Paulca
• dc.contributor.author Fernandes, Henrique M.ca
• dc.contributor.author Giovanni, Petrica
• dc.contributor.author Van Hartevelt, Tim J.ca
• dc.contributor.author Vaccarino, Francescoca
• dc.contributor.author Deco, Gustavoca
• dc.contributor.author Turkheimer, Federicoca
• dc.contributor.author Kringelbach, Morten L.ca
• dc.date.accessioned 2016-12-19T08:09:51Z
• dc.date.available 2016-12-19T08:09:51Z
• dc.date.issued 2016ca
• dc.description.abstract In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain's functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain's complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e., interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: (i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and (ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the scaffold and comparing it to local graph metrics traditionally employed in neuroimaging studies. We conclude that the persistence scaffold enables the identification of network elements that may support the functional integration of information across distributed brain networks.en
• dc.description.sponsorship LDL is supported by the Canadian Institutes of Health Research (LDL), the Canadian Centennial Scholarship Fund, and a scholarship award from Hertford College (University of Oxford). MK is supported by European Research Council (ERC) Consolidator Grant: CAREGIVING (615539). FT and PE are supported by a PET Methodology Programme grant from the Medical Research Council UK (ref no. G1100809/1). GD is supported by ERC Advanced Grant: DYSTRUCTURE (295129) and by the Spanish Research Project PSI2013-42091-P. GP and FV are supported by the TOPDRIM project supported by the Future and Emerging Technologies programme of the European Commission under Contract IST-318121.en
• dc.format.mimetype application/pdfca
• dc.identifier.citation Lord LD, Expert P, Fernades HM, Petri G, Van Hartervelt TJ, Vaccarino F, Deco G, Turkheimer F, Kringelbach ML. Insights into brain architectures from the homological scaffolds of functional connectivity networks. Front Syst Neurosci. 2016;10:85. DOI: 10.3389/fnsys.2016.00085ca
• dc.identifier.doi http://dx.doi.org/10.3389/fnsys.2016.00085
• dc.identifier.issn 1662-5137ca
• dc.identifier.uri http://hdl.handle.net/10230/27786
• dc.language.iso engca
• dc.publisher Frontiersca
• dc.relation.ispartof Frontiers in Systems Neuroscience. 2016;10:85
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/295129ca
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/615539
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/318121
• dc.relation.projectID eu-repo/grantAgreement/ES/1PE/PSI2013-42091-P
• dc.rights © 2016 Lord, Expert, Fernandes, Petri, Van Hartevelt, Vaccarino, Deco, Turkheimer and Kringelbach. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.ca
• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Functional connectivityen
• dc.subject.keyword fMRIen
• dc.subject.keyword Persistent homologyen
• dc.subject.keyword Homological scaffolden
• dc.subject.keyword Integration and segregationen
• dc.title Insights into brain architectures from the homological scaffolds of functional connectivity networksca
• dc.type info:eu-repo/semantics/articleca
• dc.type.version info:eu-repo/semantics/publishedVersionca