Multifractal organization of EEG signals in multiple sclerosis

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cris.lastimport.scopus2025-04-03T03:14:49Z
dc.abstract.enQuantifying the complex/multifractal organization of the brain signals is crucial to fully understanding the brain processes and structure. In this contribution, we performed the multifractal analysis of the electroen-cephalographic (EEG) data obtained from a controlled multiple sclerosis (MS) study, focusing on the correlation between the degree of multifractality, disease duration, and disability level. Our results reveal a significant correspondence between the complexity of the time series and multiple sclerosis development, quantified respectively by scaling exponents and the Expanded Disability Status Scale (EDSS). Namely, for some brain regions, a well-developed multifractality and little persistence of the time series were identified in patients with a high level of disability, whereas the control group and patients with low EDSS were characterized by persistence and monofractality of the signals. The analysis of the cross-correlations between EEG signals supported these results, with the most significant differences identified for patients with EDSS > 1 and the combined group of patients with EDSS ≤ 1 and controls. No association between the multifractality and disease duration was observed, indicating that the multifractal organization of the data is a hallmark of developing the disease. The observed complexity/multifractality of EEG signals is hypothetically a result of neuronal compensation – i.e., of optimizing neural processes in the presence of structural brain degeneration. The presented study is highly relevant due to the multifractal formalism used to quantify complexity and due to scarce resting-state EEG evidence for cortical reorganization associated with compensation.
dc.affiliationWydział Psychologii, Katowice
dc.contributor.authorWątorek, Marcin
dc.contributor.authorTomczyk, Wojciech
dc.contributor.authorGawłowska, Maga
dc.contributor.authorGolonka-Afek, Natalia
dc.contributor.authorŻyrkowska, Aleksandra
dc.contributor.authorMarona, Monika
dc.contributor.authorWnuk, Marcin
dc.contributor.authorSłowik, Agnieszka
dc.contributor.authorOchab, Jeremi K.
dc.contributor.authorFafrowicz, Magdalena
dc.contributor.authorMarek, Tadeusz
dc.contributor.authorOświęcimka, Paweł
dc.date.access2024-01-13
dc.date.accessioned2024-01-19T07:45:16Z
dc.date.available2024-01-19T07:45:16Z
dc.date.created2023-12-29
dc.date.issued2024-01-13
dc.description.accesstimeat_publication
dc.description.physical1-13
dc.description.versionfinal_published
dc.description.volume91
dc.identifier.doi10.1016/j.bspc.2023.105916
dc.identifier.eissn1746-8108
dc.identifier.issn1746-8094
dc.identifier.urihttps://share.swps.edu.pl/handle/swps/390
dc.identifier.weblinkhttps://www.sciencedirect.com/science/article/pii/S1746809423013496
dc.languageen
dc.pbn.affiliationpsychologia
dc.rightsOther
dc.rights.questionYes_rights
dc.rights.uriOther
dc.share.articleOTHER
dc.subject.enMultifractal
dc.subject.enTime series
dc.subject.enEEG
dc.subject.enNonlinearity
dc.subject.enComplexity
dc.swps.sciencecloudsend
dc.titleMultifractal organization of EEG signals in multiple sclerosis
dc.title.journalBiomedical Signal Processing and Control
dc.typeJournalArticle
dspace.entity.typeArticle