Characterization of the Spatial Structure of Local Functional Connectivity Using Multidistance Average Correlation Measures
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- dc.contributor.author Macià, Dídac
- dc.contributor.author Pujol Martí, Jesús, 1981-
- dc.contributor.author Blanco Hinojo, Laura, 1981-
- dc.contributor.author Martínez-Vilavella, Gerard
- dc.contributor.author Martín Santos, Rocío
- dc.contributor.author Deus, Joan
- dc.date.accessioned 2019-03-21T08:24:10Z
- dc.date.issued 2018
- dc.description.abstract There is ample evidence from basic research in neuroscience of the importance of local corticocortical networks. Millimetric resolution is achievable with current functional magnetic resonance imaging (fMRI) scanners and sequences, and consequently a number of "local" activity similarity measures have been defined to describe patterns of segregation and integration at this spatial scale. We have introduced the use of IsoDistant Average Correlation (IDAC), easily defined as the average fMRI temporal correlation of a given voxel with other voxels placed at increasingly separated isodistant intervals, to characterize the curve of local fMRI signal similarities. IDAC curves can be statistically compared using parametric multivariate statistics. Furthermore, by using red-green-blue color coding to display jointly IDAC values belonging to three different distance lags, IDAC curves can also be displayed as multidistance IDAC maps. We applied IDAC analysis to a sample of 41 subjects scanned under two different conditions, a resting state and an auditory-visual continuous stimulation. Multidistance IDAC mapping was able to discriminate between gross anatomofunctional cortical areas and, moreover, was sensitive to modulation between the two brain conditions in areas known to activate and deactivate during audiovisual tasks. Unlike previous fMRI local similarity measures already in use, our approach draws special attention to the continuous smooth pattern of local functional connectivity.
- dc.description.sponsorship This work was supported in part by the Carlos III Health Institute grant PI10/02206 and the I+D+I grant PSI2014-53524-P. We thank the Agency of University and Research Funding Management of the Catalonia Government for their participation in the context of Research Group SGR SGR2017-1198.
- dc.format.mimetype application/pdf
- dc.identifier.citation Macià D, Pujol J, Blanco-Hinojo L, Martínez-Vilavella G, Martín-Santos R, Deus J. Characterization of the Spatial Structure of Local Functional Connectivity Using Multidistance Average Correlation Measures. Brain Connect. 2018 Jun;8(5):276-287. DOI: 10.1089/brain.2017.0575
- dc.identifier.doi http://dx.doi.org/10.1089/brain.2017.0575
- dc.identifier.issn 2158-0014
- dc.identifier.uri http://hdl.handle.net/10230/36879
- dc.language.iso eng
- dc.publisher Mary Ann Liebert, Inc
- dc.relation.ispartof Brain Connectivity. 2018 Jun;8(5):276-87
- dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PSI2014-53524-P
- dc.rights Final publication is available from Mary Ann Liebert, Inc., publishers http://dx.doi.org/10.1089/brain.2017.0575
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.keyword Brain activity
- dc.subject.keyword Connectivity maps
- dc.subject.keyword FMRI
- dc.subject.keyword Local activity integration
- dc.subject.other Cervell -- Fisiologia
- dc.title Characterization of the Spatial Structure of Local Functional Connectivity Using Multidistance Average Correlation Measures
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/acceptedVersion