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A graph based characterization of functional resting state networks for patients with disorders of consciousness

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dc.creatorMartínez, Darwin E.spa
dc.creatorMartinez, Johann H.spa
dc.creatort, Jorge E. Rudasspa
dc.creatorDemertzi, Athenaspa
dc.creatorHeine, Lizettespa
dc.creatorTshibanda, Luabaspa
dc.creatorSoddu, Andreaspa
dc.creatorLaureys, Stevenspa
dc.creatorGomez, Franciscospa
dc.date.accessioned2020-05-26T00:04:35Z
dc.date.available2020-05-26T00:04:35Z
dc.date.created2015spa
dc.description.abstractDisorder of consciousness (DOC) is a consequence of severe brain injuries. Diagnosis of DOC is very challenging because it requires the patient collaboration. Research in hemodynamic brain activity in resting state conditions suggests that healthy brain is organized into large-scale resting state networks (RSNs) of sensory/cognitive relevance. Recently, relationships among these RSNs have been explored as a possible biomarker of loss of consciousness. The RSN functional connectivity is computed as the temporal relationship between pairs of RSNs time-courses. It results in the so called functional network of brain connectivity (FNC). The properties of this network in the DOC conditions remains poorly understood. In this work, we investigated some local complex network properties of the brain FNC, during altered states of consciousness. For this, we characterized a population of 49 DOC patients and 27 healthy controls. fMRI data was acquired and processed for each subject to built a FNC for each one. Network characterization was performed by computing the strength and the clustering coefficient measurements at individual level on the corresponding FNC. These nodal measurements allows to understand brain alterations of single RSN in the FNC. Our results show that strength and clustering variations may reflect brain network reconfiguration, and they may be associated to loss of consciousness states in patients with DOCs. © 2015 IEEE.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1109/STSIVA.2015.7330414
dc.identifier.issn2015
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/23696
dc.language.isoengspa
dc.publisherInstitute of Electrical and Electronics Engineers Inc.spa
dc.relation.citationTitle2015 20th Symposium on Signal Processing Images and Computer Vision STSIVA 2015 - Conference Proceedings
dc.relation.ispartof2015 20th Symposium on Signal Processing, Images and Computer Vision, STSIVA 2015 - Conference Proceedings, ISSN:2015,(2015)spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84962875990&doi=10.1109%2fSTSIVA.2015.7330414&partnerID=40&md5=3b9e332e9b27532dc562d7592170b558spa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordBrainspa
dc.subject.keywordComputer visionspa
dc.subject.keywordDiagnosisspa
dc.subject.keywordGraphic methodsspa
dc.subject.keywordImage processingspa
dc.subject.keywordSignal processingspa
dc.subject.keywordAltered states of consciousnessspa
dc.subject.keywordBrain connectivityspa
dc.subject.keywordClustering coefficientspa
dc.subject.keywordFunctional connectivityspa
dc.subject.keywordFunctional networkspa
dc.subject.keywordLoss of consciousnessspa
dc.subject.keywordNetwork characterizationspa
dc.subject.keywordTemporal relationshipsspa
dc.subject.keywordComplex networksspa
dc.titleA graph based characterization of functional resting state networks for patients with disorders of consciousnessspa
dc.typeconferenceObjecteng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaDocumento de conferenciaspa
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