Evidence map›Paper›PMID 34263439›Full record

ArticleMedical & biological engineering & computing2021

Physiological separation of Alzheimer's disease and Alzheimer's disease with significant levels of cerebrovascular symptomology and healthy controls.

Brian J Lithgow, Zeinab Dastgheib, Neda Anssari, Behzad Mansouri, Brian Blakley, Mehrangiz Ashiri, Zahra Moussavi

Abstract read
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In one paragraph

Article in Medical & biological engineering & computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 16 citations in OpenAlex.

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  5. Verification EVestG recordings are vestibuloacoustic signals.Laryngoscope investigative otolaryngology · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Brian J LithgowDiagnostic and Neurosignal Processing Research Laboratory, Biomedical Engineering Program, University of Manitoba, Riverview Health Centre, Winnipeg, MB, Canada. Brian.Lithgow@umanitoba.ca.
Zeinab DastgheibDiagnostic and Neurosignal Processing Research Laboratory, Biomedical Engineering Program, University of Manitoba, Riverview Health Centre, Winnipeg, MB, Canada.
Neda AnssariVision Matter Research Ins., Brain, Vision and Concussion Clinic-iScope, Winnipeg, MB, Canada.
Behzad MansouriDiagnostic and Neurosignal Processing Research Laboratory, Biomedical Engineering Program, University of Manitoba, Riverview Health Centre, Winnipeg, MB, Canada.
Brian BlakleyDepartment of Otolaryngology, Health Sciences Center, Winnipeg, MB, Canada.
Mehrangiz AshiriDiagnostic and Neurosignal Processing Research Laboratory, Biomedical Engineering Program, University of Manitoba, Riverview Health Centre, Winnipeg, MB, Canada.
Zahra MoussaviDiagnostic and Neurosignal Processing Research Laboratory, Biomedical Engineering Program, University of Manitoba, Riverview Health Centre, Winnipeg, MB, Canada.
University of Manitoba · CAHealth Sciences Centre · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most dementia patients with a mixed dementia (MxD) diagnosis have a mix of Alzheimer's disease (AD) and vascular dementia. Electrovestibulography (EVestG) records vestibuloacoustic afferent activity. We hypothesize EVestG recordings of AD and MxD patients are different. All patients were assessed with the Montreal cognitive assessment (MoCA) and Hachinski ischemic scale (HIS) (> 4 HIS score < 7 is representative of MxD cerebrovascular symptomology). EVestG recordings were made from 26 AD, 21 MxD and 44 healthy (control) participants. Features were derived from the EVestG recordings of the average field potential and field potential interval histogram to classify the AD, MxD and control groups. Multivariate analysis was used to test the features' significance. Using a leave-one-out cross-validated linear discriminant analysis with 3 EVestG features yielded accuracies > 80% for separating pairs of AD/MxD/control. Using the MoCA assessment and 2 EVestG features, a best accuracy of 81 to 91% depending on the classifier was obtained for the 3-way identification of AD, MxD and controls. EVestG measures provide a physiological basis for identifying AD from MxD. EVestG measures are hypothesized to be partly related to channelopathies and changes in the descending input to the vestibular periphery. Four of the five AD or MxD versus control features used had significant correlations with the MoCA. This supports assertions that the pathologic changes associated with AD impact the vestibular system and further are suggestive that the postulated physiological changes behind these features have an association with cognitive decline severity.

Indexed as

Alzheimer DiseaseCerebrovascular DisordersDementia, VascularVestibule, LabyrinthDiscriminant AnalysisHumansAlzheimer’s diseaseBiomarkersCerebrovascular symptomatologyElectrovestibulographyMixed dementiaVascular dementia

Identifiers

PMID34263439
OpenAlexW3183094115

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.