Using principal component analysis to determine which vestibular stimuli provide best biomarkers for separating Alzheimer's from mixed Alzheimer's disease.

S Marzban, Z Dastgheib, B Lithgow, Z Moussavi

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Article in Medical & biological engineering & computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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4 authors.

S MarzbanUniversity of Manitoba, Winnipeg, MB, Canada.

Z DastgheibUniversity of Manitoba, Winnipeg, MB, Canada.

B LithgowUniversity of Manitoba, Winnipeg, MB, Canada.

Z MoussaviUniversity of Manitoba, Winnipeg, MB, Canada. Zahra.Moussavi@Umanitoba.ca.ORCID http://orcid.org/0000-0001-9202-949X

Funding

Mitacs IT26869

6 · The paper itself

Abstract

Alzheimer's disease (AD) is often mixed with cerebrovascular disease (AD-CVD). Heterogeneity of dementia etiology and the overlapping of neuropathological features of AD and AD-CVD make feature identification of the two challenging. Separation of AD from AD-CVD is important as the optimized treatment for each group may differ. Recent studies using vestibular responses recorded from electrovestibulography (EVestG™) have offered promising results for separating these two pathologies. An EVestG measurement records responses to several different physical stimuli (called tilts). In previous research, the number of EVestG features from different tilts was selected based on physiological intuition to classify AD from AD-CVD. As the number of potential characteristic features from all tilts can be very large, in this study, we used an algorithm based on principal component analysis (PCA) to rank the most effective vestibular stimuli for differentiating AD from AD-CVD. Analyses were performed on the EVestG signals of 28 individuals with AD and 24 with AD-CVD. The results of this study showed that tilts simulating the otolithic organs (utricle and saccule) generated the most characteristic features for separating AD from AD-CVD.

Indexed as

Alzheimer DiseasePrincipal Component AnalysisVestibule, LabyrinthAgedAged, 80 and overAlgorithmsBiomarkersCerebrovascular DisordersDiagnosis, DifferentialFemaleHumansMaleMiddle AgedVestibular Function TestsBiomarkersAlzheimer’s diseaseAutomated tilt selectionCerebrovascular symptomatologyElectrovestibulographyPrincipal component analysis (PCA)

Identifiers

PMID38735986

DOI10.1007/s11517-024-03110-2

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.