Task-specific dynamical entropy variations in EEG as a biomarker for Parkinson's disease progression

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dc.authorid0000-0003-1396-2885
dc.contributor.authorOnay, Fatih
dc.contributor.authorKaracali, Bilge
dc.date.accessioned2026-02-08T15:15:00Z
dc.date.available2026-02-08T15:15:00Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractUncovering the neuronal mechanisms un-derlying optimal behavioral performance is essential to understand how the brain dynamically adapts to changing conditions. In Parkinson's disease (PD), these neuronal mechanisms are disrupted and lead to impairments in motor coordination and higher-order cognitive functions. This study investigates neuronal dynamics during a lower-limb pedaling task by analyzing the dynamical entropy of EEG signals in healthy controls (HC), PD patients, and PD patients with freezing of gait (PDFOG). We examined both average entropy changes and entropy variability across trials to characterize task-specific neural adaptations across disease progression. Results showed that PD and PDFOG patients exhibited decreased levels of permutation entropy in frontal and parietal regions, which may be associated with loss of cognitive adapta-tion due to altered information processing. Additionally, Vasicek's entropy variability in both PD groups was significantly diminished in occipital and left frontal regions, suggesting reduced cognitive capacity to dy-namically allocate neuronal resources during task engagement. We extended this analysis to the classification of groups using LDA and SVM classifiers, where entropy-derived features achieved a classification accuracy of up to 96.15% when distinguishing HC from PDFOG patients. This dynamical entropic framework provides a novel approach for capturing neural complexity changes during task performance, revealing subtle cognitive-motor impairments in PD. Understanding the maintenance of cognitive information processing and flexibility in response to motor and cognitive task demands could be a useful tool to track PD diagnosis and progression in addition to resting-state analyses.
dc.identifier.doi10.1007/s11357-025-01821-4
dc.identifier.issn2509-2715
dc.identifier.issn2509-2723
dc.identifier.pmid40728819
dc.identifier.scopus2-s2.0-105012198586
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s11357-025-01821-4
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5555
dc.identifier.wosWOS:001538529400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofGeroscience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectEntropy
dc.subjectComplexity
dc.subjectParkinson's disease
dc.subjectEEG
dc.subjectPedaling
dc.subjectTask engagement
dc.titleTask-specific dynamical entropy variations in EEG as a biomarker for Parkinson's disease progression
dc.typeArticle

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