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A brain-age model for preterm infants based on functional connectivity

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dc.creatorCaicedo Dorado, Alexander
dc.creatorJansen, Kspa
dc.creatorDereymaeker, Aspa
dc.creatorNaulaers, Gspa
dc.creatorVan Huffel, Sspa
dc.creatorLavanga, Mspa
dc.creatorDe Wel, Ospa
dc.date.accessioned2020-08-19T14:41:42Z
dc.date.available2020-08-19T14:41:42Z
dc.date.created2018-04-26spa
dc.description.abstractIn this study, the development of EEG functional connectivity during early development has been investigated in order to provide a predictive age model for premature infants. Approach: The functional connectivity has been assessed via the coherency function (its imaginary part (ImCoh) and its mean squared magnitude (MSC)), the phase locking value () and the Hilbert–Schimdt dependence (HSD) in a dataset of 30 patients, partially described and employed in previous studies (Koolen et al 2016 Neuroscience 322 298–307; Lavanga et al 2017 Complexity 2017 1–13). Infants' post-menstrual age (PMA) ranges from 27 to 42 weeks. The topology of the EEG couplings has been investigated via graph-theory indices. Main results: Results show a sharp decrease in ImCoh indices in ?, (4–8) Hz and ?, (8–16) Hz bands and MSC in ?, (16–32) Hz band with maturation, while a more modest positive correlation with PMA is found for HSD, and MSC in , ?, ? bands. The best performances for the PMA prediction were mean absolute error equal to 1.51 weeks and adjusted coefficient of determination equal to 0.8. Significance: The reported findings suggest a segregation of the cortex connectivity, which favours a diffused tasks architecture on the brain scalp. In summary, the results indicate that the neonates' brain development can be described via lagged-interaction network features.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1088/1361-6579/aabac4
dc.identifier.issnISSN: 0967-3334
dc.identifier.issnEISSN: 1361-6579
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/27309
dc.language.isoengspa
dc.publisherIOPsciencespa
dc.relation.citationIssueNo. 4
dc.relation.citationTitlePhysiological Measurement
dc.relation.citationVolumeVol. 39
dc.relation.ispartofPhysiological Measurement, ISSN: 0967-3334; EISSN: 1361-6579, Vol.39, No.4 (2018); Art. 044006spa
dc.relation.urihttps://iopscience.iop.org/article/10.1088/1361-6579/aabac4/pdfspa
dc.rights.accesRightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.accesoRestringido (Acceso a grupos específicos)spa
dc.sourcePhysiological Measurementspa
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subject.keywordstudyspa
dc.subject.keywordpremature infantsspa
dc.subject.keywordfunctional connectivityspa
dc.subject.keywordCoherency functionspa
dc.titleA brain-age model for preterm infants based on functional connectivityspa
dc.title.TranslatedTitleUn modelo de edad cerebral para bebés prematuros basado en la conectividad funcionalspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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