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dc.contributor.author
Tagliazucchi, Enzo
dc.contributor.author
Siniatchkin, Michael
dc.contributor.author
Laufs, Helmut
dc.contributor.author
Chialvo, Dante Renato
dc.date.available
2020-09-25T20:55:45Z
dc.date.issued
2016-08
dc.identifier.citation
Tagliazucchi, Enzo; Siniatchkin, Michael; Laufs, Helmut; Chialvo, Dante Renato; The voxel-wise functional connectome can be efficiently derived from co-activations in a sparse spatio-temporal point-process; Frontiers Media S.A.; Frontiers in Neuroscience; 10; 8-2016; 1-39
dc.identifier.issn
1662-453X
dc.identifier.uri
http://hdl.handle.net/11336/114912
dc.description.abstract
Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions based on large neuroimaging databases. The exploratory unraveling of this "functional connectome" based on functional Magnetic Resonance Imaging (fMRI) can benefit from a better understanding of the contributors to resting state functional connectivity. In this work, we introduce a sparse representation of fMRI data in the form of a discrete point-process encoding high-amplitude events in the blood oxygenation level-dependent (BOLD) signal and we show it contains sufficient information for the estimation of functional connectivity between all pairs of voxels. We validate this method by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n = 71), we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n = 1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring only data on the order of 1% of the original BOLD signal time series. Furthermore, we establish that the point-process approach does not reduce (and in one case increases) classification accuracy compared to standard linear correlations. Our results show how large fMRI datasets can be drastically simplified to include only the timings of large-amplitude events, while still allowing the recovery of all pair-wise interactions between voxels. The practical importance of this dimensionality reduction is manifest in the increasing number of collaborative efforts aiming to study large cohorts of healthy subjects as well as patients suffering from brain disease. Our method also suggests that the electrophysiological signals underlying the dynamics of fMRI time series consist of all-or-none temporally localized events, analogous to the avalanches of neural activity observed in recordings of local field potentials (LFP), an observation of potentially high neurobiological relevance.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Frontiers Media S.A.
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DIMENSIONALITY REDUCTION
dc.subject
FUNCTIONAL CONNECTIVITY
dc.subject
FUNCTIONAL CONNECTOME
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POINT PROCESS
dc.subject
RESTING STATE FMRI
dc.subject.classification
Otras Ciencias Físicas
dc.subject.classification
Ciencias Físicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
The voxel-wise functional connectome can be efficiently derived from co-activations in a sparse spatio-temporal point-process
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-09-24T17:31:44Z
dc.journal.volume
10
dc.journal.pagination
1-39
dc.journal.pais
Suiza
dc.journal.ciudad
Lausanne
dc.description.fil
Fil: Tagliazucchi, Enzo. Christian Albrechts Universitat Zu Kiel.; Alemania. University Frankfurt am Main; Alemania
dc.description.fil
Fil: Siniatchkin, Michael. Christian Albrechts Universitat Zu Kiel.; Alemania
dc.description.fil
Fil: Laufs, Helmut. University Frankfurt am Main; Alemania. University Hospital Schleswig Holstein; Alemania
dc.description.fil
Fil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina
dc.journal.title
Frontiers in Neuroscience
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://journal.frontiersin.org/article/10.3389/fnins.2016.00381/abstract#
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.3389/fnins.2016.00381
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