Evidence map›Paper›PMID 42740027›Full record

ArticleSensors (Basel, Switzerland)2026

Electrodermal Activity as a Potential Diagnostic Biomarker for Alzheimer's Disease: A Pilot Study.

Sibi Pandian, Luis R Mercado-Diaz, Jonathan Ruiz-Trivino, David Aguillon, Hugo Posada-Quintero

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Sibi PandianBiomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA.ORCID 0009-0000-2640-655X
Luis R Mercado-DiazBiomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA.ORCID 0000-0003-3543-3677
Jonathan Ruiz-TrivinoGrupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellín 050010, Colombia.ORCID 0009-0008-1180-0923
David AguillonGrupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellín 050010, Colombia.ORCID 0000-0003-2283-536X
Hugo Posada-QuinteroBiomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA.ORCID 0000-0003-4514-4772

Funding

Objective Integrated Multimodal Electrophysiological Index for the Quantification of Visceral PainR01DK138248 · NIDDK · UNIVERSITY OF CONNECTICUT STORRS · PI Hugo Fernando Posada-Quintero · 2024 to 2026
$1.9M
NIDDK NIH HHS R01 DK138248NIDDK NIH HHS R01DK138248
6 · The paper itself

Abstract

Alzheimer's disease (AD), the leading cause of dementia, lacks an inexpensive and non-invasive method for early detection. Here, we investigate whether electrodermal activity (EDA), a measure of changes in electrical conductance at the skin's surface, can offer a convenient avenue for AD screening by capturing the autonomic dysfunction associated with the disease's pathology. As few studies have comprehensively examined EDA's utility as a standalone biomarker for AD, we conducted a pilot study with 10 cognitively healthy controls and 20 patients with AD, evaluating EDA recordings collected during an orthostatic test and a Montreal Cognitive Assessment (MoCA). The AD group included both carriers from Colombian kindreds affected by autosomal-dominant AD and patients with sporadic late-onset AD. We compared EDA features between groups using statistical analysis and tested whether machine learning models could separate the two groups using these features. During the MoCA, phasic EDA measures, specifically the number of skin conductance responses (NSSCR) and mean time-variant sympathetic tone (TVSymp), were significantly lower in the AD group than in controls (NSSCR: p=0.0083, Cohen's d=-1.07; mean TVSymp: p=0.0166, d=-0.96), suggesting a blunted sympathetic response to cognitive stress in AD. No significant between-group differences were observed during the orthostatic test (all p≥0.60). A random forest classifier trained on MoCA-derived EDA features achieved a balanced accuracy of 78% (95% CI 60 to 93%; 75% sensitivity, 80% specificity, AUROC 0.75), with phasic features contributing most to performance. Performance did not differ significantly from that of the support vector machine or logistic regression models. These results provide preliminary, hypothesis-generating evidence for the utility of EDA in screening for AD. The small sample size, unequal group allocation, sex imbalance, and substantial mean age disparity between groups (≈23.5 years) limit the strength of these conclusions and necessitate replication in larger, age- and sex-matched cohorts that include prodromal stages.

Indexed as

Alzheimer DiseaseBiomarkersGalvanic Skin ResponseAgedFemaleHumansMachine LearningMaleMiddle AgedPilot ProjectsBiomarkersAlzheimer’s disease (AD)autonomic dysfunctionbiomarkerselectrodermal activitymachine learningsignal processing

Identifiers

PMID42740027
PMCPMC13568057

What OpenQuestion holds

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Registered trials

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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.