Academic Journal
Neural features underlying high-accuracy classification of amnestic mild cognitive impairment using multi-sensory-evoked potentials.
| Τίτλος: | Neural features underlying high-accuracy classification of amnestic mild cognitive impairment using multi-sensory-evoked potentials. |
|---|---|
| Συγγραφείς: | Zhang L; Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada. lzhang@research.baycrest.org., Binns M; Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5T 3M7, Canada., Chow R; Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada., Rabi R; Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada., Anderson ND; Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.; Department of Psychology, University of Toronto, Toronto, ON, M5G 3G3, Canada.; Department of Psychiatry, University of Toronto, Toronto, ON, M5T 1R8, Canada., Lu J; MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China., Freedman M; Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.; Department of Medicine (Neurology), Baycrest Centre, Toronto, ON, M6A 2E1, Canada.; Department of Medicine (Neurology), Mount Sinai Hospital, Toronto, ON, M5G 1X5, Canada.; Department of Medicine (Neurology), University of Toronto, Toronto, ON, M5S 3H2, Canada., Alain C; Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada. calain@research.baycrest.org.; Department of Psychology, University of Toronto, Toronto, ON, M5G 3G3, Canada. calain@research.baycrest.org.; Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada. calain@research.baycrest.org.; Music and Health Science Research Collaboratory, University of Toronto, Toronto, ON, M5S 2C3, Canada. calain@research.baycrest.org. |
| Πηγή: | GeroScience [Geroscience] 2026 Apr; Vol. 48 (2), pp. 2545-2562. Date of Electronic Publication: 2025 Jul 22. |
| Τύπος έκδοσης: | Journal Article; Research Support, Non-U.S. Gov't |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer International Publishing Country of Publication: Switzerland NLM ID: 101686284 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2509-2723 (Electronic) Linking ISSN: 25092723 NLM ISO Abbreviation: Geroscience Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Cham : Springer International Publishing, [2017]- |
| Ιατρικοί όροι (MeSH): | Cognitive Dysfunction*/diagnosis , Cognitive Dysfunction*/physiopathology , Cognitive Dysfunction*/classification , Amnesia*/physiopathology , Amnesia*/diagnosis , Evoked Potentials, Somatosensory*/physiology , Evoked Potentials*/physiology, Electroencephalography/methods ; Humans ; Aged ; Male ; Female ; Support Vector Machine ; Aged, 80 and over ; Case-Control Studies ; Sensitivity and Specificity ; Middle Aged |
| Περίληψη: | 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. (© 2025. The Author(s), under exclusive licence to American Aging Association.) |
| Competing Interests: | Declarations. Competing interests: The authors declare no competing interests. |
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| Grant Information: | 194536 Natural Sciences and Engineering Research Council of Canada; 05241 Natural Sciences and Engineering Research Council of Canada; PJT 183614 Canadian Institute for Health Research |
| Contributed Indexing: | Keywords: Aging; EEG; Evoked potentials; Machine learning; Mild cognitive impairment; Sensory perception |
| Entry Date(s): | Date Created: 20250722 Date Completed: 20260309 Latest Revision: 20260813 |
| Update Code: | 20260813 |
| PubMed Central ID: | PMC12972424 |
| DOI: | 10.1007/s11357-025-01799-z |
| PMID: | 40696071 |
| Βάση Δεδομένων: | MEDLINE |
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