Brain sources composing irregular field potentials have unique temporal signatures

dc.contributor.authorMuñoz Arnaiz, Ricardo
dc.contributor.authorMakarova, Julia
dc.contributor.authorMakarov Slizneva, Valeriy
dc.contributor.authorHerreras, Oscar
dc.date.accessioned2026-01-14T13:55:13Z
dc.date.available2026-01-14T13:55:13Z
dc.date.issued2025
dc.description.abstractThe prevailing irregular pattern of field potentials is little used due to the uncertain origin and identity of the source populations. After recovering clean source-specific dynamics (field potential-generators) in multiple brain areas of anesthetized rats we explored if they contain temporal identity features and to what extent they remain upon blending in the volume (raw field potentials). Relevant factors and mechanisms were further explored through a feed-forward model of field potentials. Signals were characterized with a multivariate set of statistical, spectral and nonlinear measures and explored with machine-learning classifiers. Despite the strong variability of electrographic patterns, field potential generators exhibit unique temporal signatures that allow their discrimination. Signatures are contained in 1 to 5 s segments in any given brain region and are robust across groups of animals. In contrast, the spatial overlap of sources and the contribution by remote potentials cause indeterminacy of raw field potentials, making them approach a noisy behavior. The so revealed source-specific signatures contain spectral and nonlinear features, thus overcoming the traditional notion of waves and frequency bands. We propose that besides upstream dynamics cytoarchitectural factors of the source population contribute to these unique signatures. These findings pave the way to utilize the vast reserve of information contained in irregular field potentials.
dc.description.departmentDepto. de Análisis Matemático y Matemática Aplicada
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Ciencia e Innovación
dc.description.sponsorshipEuropean Commission
dc.description.sponsorshipAgencia Estatal de Evaluación
dc.description.sponsorshipComunidad Autónoma de Madrid
dc.description.statuspub
dc.identifier.doi10.1093/cercor/bhaf135
dc.identifier.officialurlhttps://doi.org/10.1093/cercor/bhaf135
dc.identifier.urihttps://hdl.handle.net/20.500.14352/130216
dc.issue.number6
dc.journal.titleCerebral Cortex
dc.language.isoeng
dc.publisherOxford University Press
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137801NB-I00/ES/TECNOLOGIA PARA DISCRIMINAR Y MODULAR ACTIVIDAD CEREBRAL IRREGULAR BASAL Y DISFUNCIONAL EN HUMANO Y RATA: FUENTES PROFUNDAS, DIMENSION FRACTAL Y ESTIMULACION LASER/
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-124047NB-I00/ES/FUNDAMENTOS MATEMATICOS DE LA COGNICION PROFUNDA: HACIA EL DESARROLLO DE AGENTES AUTONOMOS BIOINSPIRADOS/
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PDC2021-121103-I00/ES/LOCALIZACION AUTOMATIZADA DE REGIONES CEREBRALES SUBLOBULARES CON ACTIVIDAD BIOELECTRICA PATOLOGICA MEDIANTE SEPARACION CIEGA DE FUENTES Y APRENDIZAJE PROFUNDO/
dc.relation.projectIDEU 2020/2094-IASOMM24006
dc.relation.projectIDPIPF-2023/SAL-GL-30443
dc.rights.accessRightsopen access
dc.subject.keywordAperiodic activity
dc.subject.keywordDeep source separation
dc.subject.keywordIndependent component analysis
dc.subject.keywordSupervised machine learning
dc.subject.keywordVolume-conduction
dc.subject.ucmNeurociencias (Biológicas)
dc.subject.unesco2490.01 Neurofisiología
dc.titleBrain sources composing irregular field potentials have unique temporal signatures
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number35
dspace.entity.typePublication
relation.isAuthorOfPublicationa5728eb3-1e14-4d59-9d6f-d7aa78f88594
relation.isAuthorOfPublication.latestForDiscoverya5728eb3-1e14-4d59-9d6f-d7aa78f88594

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