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Analysis of the middle and long latency ERP components in Schizophrenia

2021Conference paper Restricted access
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dc.contributor.authorCosta, Miguel Rocha
dc.contributor.authorTeixeira, Felipe
dc.contributor.authorTeixeira, João Paulo
dc.date.accessioned2022-04-05T14:23:31Z
dc.date.available2022-04-05T14:23:31Z
dc.date.issued2021
dc.description.abstractSchizophrenia is a complex and disabling mental disorder estimated to affect 21million people worldwide. Electroencephalography (EEG) has proven to be an excellent tool to improve and aid the current diagnosis of mental disorders such as schizophrenia. The illness is comprised of various disabilities associated with sensory processing and perception. In this work, the first 10−200 ms of brain activity after the self-generation via button presses (condition 1) and passive presentation (condition 2) of auditory stimuli was addressed. A time-domain analysis of the event-related potentials (ERPs), specifically the MLAEP, N1, and P2 components, was conducted on 49 schizophrenic patients (SZ) and 32 healthy controls (HC), provided by a public dataset. The amplitudes, latencies, and scalp distribution of the peaks were used to compare groups. Suppression, measured as the difference between both conditions’ neural activity, was also evaluated. With the exception of the N1 peak during condition (1), patients exhibited significantly reduced amplitudes in all waveforms analyzed in both conditions. The SZ group also demonstrated a peak delay in theMLAEP during condition (2) and amodestly earlier P2 peak during condition (1). Furthermore, patients exhibited less andmore N1 and P2 suppression, respectively. Finally, the spatial distribution of activity in the scalp during the MLAEP peak in both conditions, N1 peak in condition (1) and N1 suppression differed considerably between groups. These findings and measurements will be used with the finality of developing an intelligent system capable of accurately diagnosing schizophrenia.pt_PT
dc.description.sponsorshipThis article is a result of the project “GreenHealth - Digital strategies in biological assets to improve well-being and promote green health” (Norte-01-0145-FEDER-000042), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 PartnershipAgreement, through the European Regional Development Fund (ERDF).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCosta, Miguel Rocha e; Teixeira, Felipe; Teixeira, João Paulo (2021). Analysis of the middle and long latency ERP components in Schizophrenia. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 477-491. ISBN 978-3-030-91884-2pt_PT
dc.identifier.doi10.1007/978-3-030-91885-9_35pt_PT
dc.identifier.isbn978-3-030-91884-2
dc.identifier.urihttp://hdl.handle.net/10198/25360
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSchizophreniapt_PT
dc.subjectEEGpt_PT
dc.subjectERPpt_PT
dc.subjectMLAEPpt_PT
dc.subjectP2pt_PT
dc.subjectN1pt_PT
dc.titleAnalysis of the middle and long latency ERP components in Schizophreniapt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage491pt_PT
oaire.citation.startPage477pt_PT
oaire.citation.titleOptimization, learning algorithms and applications: first International Conference, OL2A 2021pt_PT
oaire.citation.volume1488pt_PT
person.familyNameTeixeira
person.familyNameTeixeira
person.givenNameFelipe
person.givenNameJoão Paulo
person.identifier663194
person.identifier.ciencia-id0E17-62FB-AA17
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id57069567500
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication764c5209-b9ab-479e-b5be-59fbe07c784b
relation.isAuthorOfPublication33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isAuthorOfPublication.latestForDiscovery33f4af65-7ddf-46f0-8b44-a7470a8ba2bf

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