Eeg biomarkers in alzheimer's and prodromal alzheimer's: a comprehensive analysis of spectral and connectivity features

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dc.contributor.authorChetty, Chowtapalle Anuraag
dc.contributor.authorBhardwaj, Harsha
dc.contributor.authorKumar, G. Pradeep
dc.contributor.authorDevanand, T.
dc.contributor.authorAktürk, Tuba
dc.contributor.authorGüntekin, Bahar
dc.contributor.authorAdaikkan, Chinnakkaruppan
dc.date.accessioned2025-10-21T13:02:04Z
dc.date.available2025-10-21T13:02:04Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Rektörlük, Sağlık Bilim ve Teknolojileri Araştırma Enstitüsü
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Biyofizik Ana Bilim Dalı
dc.description.abstractBackground: Biomarkers of Alzheimer’s disease (AD) and mild cognitive impairment (MCI, or prodromal AD) are highly significant for early diagnosis, clinical trials and treatment outcome evaluations. Electroencephalography (EEG), being noninvasive and easily accessible, has recently been the center of focus. However, a comprehensive understanding of EEG in dementia is still needed. A primary objective of this study is to investigate which of the many EEG characteristics could effectively differentiate between individuals with AD or prodromal AD and healthy individuals. Methods: We collected resting state EEG data from individuals with AD, prodromal AD, and normal cognition. Two distinct preprocessing pipelines were employed to study the reliability of the extracted measures across different datasets. We extracted 41 different EEG features. We have also developed a stand-alone software application package, Feature Analyzer, as a comprehensive toolbox for EEG analysis. This tool allows users to extract 41 EEG features spanning various domains, including complexity measures, wavelet features, spectral power ratios, and entropy measures. We performed statistical tests to investigate the differences in AD or prodromal AD from age-matched cognitively normal individuals based on the extracted EEG features, power spectral density (PSD), and EEG functional connectivity. Results: Spectral power ratio measures such as theta/alpha and theta/beta power ratios showed significant differences between cognitively normal and AD individuals. Theta power was higher in AD, suggesting a slowing of oscillations in AD; however, the functional connectivity of the theta band was decreased in AD individuals. In contrast, we observed increased gamma/alpha power ratio, gamma power, and gamma functional connectivity in prodromal AD. Entropy and complexity measures after correcting for multiple electrode comparisons did not show differences in AD or prodromal AD groups. We thus catalogued AD and prodromal AD-specific EEG features. Conclusions: Our findings reveal that the changes in power and connectivity in certain frequency bands of EEG differ in prodromal AD and AD. The spectral power, power ratios, and the functional connectivity of theta and gamma could be biomarkers for diagnosis of AD and prodromal AD, measure the treatment outcome, and possibly a target for brain stimulation.
dc.description.sponsorshipCBR start-up fund (CA) and the India Alliance DBT Wellcome Trust ; Council of Scientific & Industrial Research (CSIR) - India ; Brain and Behavior Research Foundation ; Wellcome Trust
dc.identifier.citationChetty, C. A., Bhardwaj, H., Kumar, G. P., Devanand T., Aktürk, T., Güntekin, B. ... Adaikkan, C. (2024). Eeg biomarkers in alzheimer's and prodromal alzheimer's: a comprehensive analysis of spectral and connectivity features. Alzheimer's Research and Therapy, 16(1). http://dx.doi.org/10.1186/s13195-024-01582-w
dc.identifier.doi10.1186/s13195-024-01582-w
dc.identifier.issn1758-9193
dc.identifier.issue1
dc.identifier.pmid39449097
dc.identifier.scopus2-s2.0-85207348853
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1186/s13195-024-01582-w
dc.identifier.urihttps://hdl.handle.net/20.500.12511/13130
dc.identifier.volume16
dc.identifier.wosWOS:001340656800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAktürk, Tuba
dc.institutionauthorGüntekin, Bahar
dc.institutionauthorid0000-0002-7555-3801
dc.institutionauthorid0000-0002-0860-0524
dc.language.isoen
dc.relation.ecinfo:eu-repo/grantAgreement/EC/FP7/09/1233(16115)/2022-EMR-I
dc.relation.ecinfo:eu-repo/grantAgreement/EC/FP7/09/1233(15927)/2022-EMR-I
dc.relation.ecinfo:eu-repo/grantAgreement/EC/FP7/31590
dc.relation.ecinfo:eu-repo/grantAgreement/EC/FP7/A/E/22/1/506784
dc.relation.ispartofAlzheimer's Research and Therapy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAging
dc.subjectBrain Connectivity
dc.subjectEEG-Based Biomarker
dc.subjectEyes Closed EEG
dc.subjectGamma
dc.subjectPairwise Phase Consistency
dc.subjectSlowing Of Oscillations
dc.subjectTheta-Alpha Power Ratio
dc.titleEeg biomarkers in alzheimer's and prodromal alzheimer's: a comprehensive analysis of spectral and connectivity features
dc.typeArticle

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