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dc.contributor.authorQuintero Zea, Andrés-
dc.contributor.authorLópez Hincapié, Jose David-
dc.contributor.authorSmith, Keith-
dc.contributor.authorTrujillo Orrego, Natalia-
dc.contributor.authorParra Rodríguez, Mario Alfredo-
dc.contributor.authorEscudero Ordoñez, Javier Antonio-
dc.date.accessioned2023-11-13T19:15:21Z-
dc.date.available2023-11-13T19:15:21Z-
dc.date.issued2018-
dc.identifier.citationA. Quintero-Zea, J. D. LóPez, K. Smith, N. Trujillo, M. A. Parra and J. Escudero, "Phenotyping Ex-Combatants From EEG Scalp Connectivity," in IEEE Access, vol. 6, pp. 55090-55098, 2018, doi: 10.1109/ACCESS.2018.2872765.spa
dc.identifier.urihttps://hdl.handle.net/10495/37293-
dc.description.abstractABSTRACT: Being involved in war experiences may have severe consequences in mental health. This exposure has been associated in Colombian ex-combatants with risk of proactive aggression modulating emotional processing. However, the extent of the cognitive processes underlying aggressive behavior is still an open issue. In this paper, we propose a support vector machine-based processing pipeline to identify different cognitive phenotypes associated with atypical emotional processing, based on canonical correlation analysis of EEG network features, and cognitive and behavioral evaluations. Results show the existence of cognitive phenotypes associated with differences in the mean value of leaf fraction and diameter of EEG networks across groups. The ability of identifying phenotypes in these otherwise healthy subjects opens up the possibility to aid in the development of specific interventions aimed to reduce expression of proactive aggression in ex-combatants and assessing the efficacy of such interventions.spa
dc.format.extent9spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherInstitute of Electrical and Electronics Engineersspa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersionspa
dc.rightsinfo:eu-repo/semantics/openAccessspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titlePhenotyping Ex-Combatants From EEG Scalp Connectivityspa
dc.typeinfo:eu-repo/semantics/articlespa
dc.publisher.groupSalud Mentalspa
dc.publisher.groupSistemas Embebidos e Inteligencia Computacional (SISTEMIC)spa
dc.identifier.doi10.1109/ACCESS.2018.2872765-
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
dc.identifier.eissn2169-3536-
oaire.citationtitleIEEE Accessspa
oaire.citationstartpage55090spa
oaire.citationendpage55098spa
oaire.citationvolume6spa
dc.rights.creativecommonshttps://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.publisher.placePiscataway, Estados Unidosspa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.type.redcolhttps://purl.org/redcol/resource_type/ARTspa
dc.type.localArtículo de investigaciónspa
dc.subject.decsElectroencefalografía-
dc.subject.decsElectroencephalography-
dc.subject.decsFenotipo-
dc.subject.decsPhenotype-
dc.subject.decsAprendizaje Automático-
dc.subject.decsMachine Learning-
dc.subject.lembTeoría de grafos-
dc.subject.lembGraph theory-
dc.subject.lembCuero cabelludo-
dc.subject.lembScalp-
dc.subject.proposalEx-combatantsspa
dc.subject.proposalExcombatientesspa
dc.description.researchgroupidCOL0010717spa
dc.description.researchgroupidCOL0015983spa
dc.relation.ispartofjournalabbrevIEEE Accessspa
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