Evidence map›Paper›PMID 40696071›Full record

ArticleGeroScience2026

Neural features underlying high-accuracy classification of amnestic mild cognitive impairment using multi-sensory-evoked potentials.

Lei Zhang, Malcolm Binns, Ricky Chow, Rahel Rabi, Nicole D Anderson, Jing Lu, Morris Freedman, Claude Alain

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GeroScience, 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

8 authors.

Lei ZhangRotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada. lzhang@research.baycrest.org.ORCID 0000-0001-6518-2018
Malcolm BinnsRotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.
Ricky ChowRotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.
Rahel RabiRotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.
Nicole D AndersonRotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.
Jing LuMOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Morris FreedmanRotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.
Claude AlainRotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada. calain@research.baycrest.org.

Funding

Canadian Institute for Health Research PJT 183614Natural Sciences and Engineering Research Council of Canada 05241Natural Sciences and Engineering Research Council of Canada 194536
6 · The paper itself

Abstract

Early detection of amnestic mild cognitive impairment (aMCI) is crucial for timely interventions. This study combines scalp recordings of lateralized auditory, visual, and somatosensory stimuli with a flexible and interpretable support vector machine learning pipeline to differentiate individuals diagnosed with aMCI from healthy controls. Event-related potentials (ERPs) and functional connectivity (FC) matrices from each modality successfully predicted aMCI. Reduced ERP amplitude in aMCI contributed to classification. The analysis of FC using phase-locking value revealed higher FC in aMCI than controls in frontal regions, which predicted worse cognitive performance, and lower FC in posterior regions from delta to alpha frequency. We observe optimal classification accuracy (96.1%), sensitivity (97.7%), and specificity (94.3%) when combining information from all sensory conditions rather than when using information from a single modality. The results highlight the clinical potential of sensory-evoked potentials in detecting aMCI, with optimal classification using both amplitude and oscillatory-based FC measures from multiple modalities.

Indexed as

AmnesiaCognitive DysfunctionEvoked PotentialsEvoked Potentials, SomatosensoryAgedAged, 80 and overCase-Control StudiesElectroencephalographyFemaleHumansMaleMiddle AgedSensitivity and SpecificitySupport Vector MachineAgingEEGEvoked potentialsMachine learningMild cognitive impairmentSensory perception

Identifiers

PMID40696071
PMCPMC12972424

What Socratic holds

Textmetadata
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 Socratic graph.