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

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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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  – Url: https://dx.doi.org/doi:10.1007/s11357-025-01799-z
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  Data: Neural features underlying high-accuracy classification of amnestic mild cognitive impairment using multi-sensory-evoked potentials.
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  Data: <searchLink fieldCode="AU" term="%22Zhang+L%22">Zhang L</searchLink>; Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada. lzhang@research.baycrest.org.<br /><searchLink fieldCode="AU" term="%22Binns+M%22">Binns M</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Chow+R%22">Chow R</searchLink>; Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.<br /><searchLink fieldCode="AU" term="%22Rabi+R%22">Rabi R</searchLink>; Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.<br /><searchLink fieldCode="AU" term="%22Anderson+ND%22">Anderson ND</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Lu+J%22">Lu J</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Freedman+M%22">Freedman M</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Alain+C%22">Alain C</searchLink>; 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.
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  Data: <searchLink fieldCode="MM" term="%22Cognitive+Dysfunction%22">Cognitive Dysfunction*</searchLink>/<searchLink fieldCode="MM" term="%22Cognitive+Dysfunction+diagnosis%22">diagnosis</searchLink> <br /><searchLink fieldCode="MM" term="%22Cognitive+Dysfunction%22">Cognitive Dysfunction*</searchLink>/<searchLink fieldCode="MM" term="%22Cognitive+Dysfunction+physiopathology%22">physiopathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Cognitive+Dysfunction%22">Cognitive Dysfunction*</searchLink>/<searchLink fieldCode="MM" term="%22Cognitive+Dysfunction+classification%22">classification</searchLink> <br /><searchLink fieldCode="MM" term="%22Amnesia%22">Amnesia*</searchLink>/<searchLink fieldCode="MM" term="%22Amnesia+physiopathology%22">physiopathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Amnesia%22">Amnesia*</searchLink>/<searchLink fieldCode="MM" term="%22Amnesia+diagnosis%22">diagnosis</searchLink> <br /><searchLink fieldCode="MM" term="%22Evoked+Potentials%2C+Somatosensory%22">Evoked Potentials, Somatosensory*</searchLink>/<searchLink fieldCode="MM" term="%22Evoked+Potentials%2C+Somatosensory+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Evoked+Potentials%22">Evoked Potentials*</searchLink>/<searchLink fieldCode="MM" term="%22Evoked+Potentials+physiology%22">physiology</searchLink><br /><searchLink fieldCode="MH" term="%22Electroencephalography%22">Electroencephalography</searchLink>/<searchLink fieldCode="MH" term="%22Electroencephalography+methods%22">methods</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Aged%22">Aged</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Support+Vector+Machine%22">Support Vector Machine</searchLink> ; <searchLink fieldCode="MH" term="%22Aged%2C+80+and+over%22">Aged, 80 and over</searchLink> ; <searchLink fieldCode="MH" term="%22Case-Control+Studies%22">Case-Control Studies</searchLink> ; <searchLink fieldCode="MH" term="%22Sensitivity+and+Specificity%22">Sensitivity and Specificity</searchLink> ; <searchLink fieldCode="MH" term="%22Middle+Aged%22">Middle Aged</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.<br /> (© 2025. The Author(s), under exclusive licence to American Aging Association.)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: Declarations. Competing interests: The authors declare no competing interests.
– Name: Ref
  Label: References
  Group: RefInfo
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  Group: Grant
  Data: 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
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  Data: <i>Keywords: </i>Aging; EEG; Evoked potentials; Machine learning; Mild cognitive impairment; Sensory perception
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  Data: <i>Date Created: </i>20250722 <i>Date Completed: </i>20260309 <i>Latest Revision: </i>20260813
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  Data: 10.1007/s11357-025-01799-z
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        Value: 10.1007/s11357-025-01799-z
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        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 2545
    Subjects:
      – SubjectFull: Electroencephalography methods
        Type: general
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Aged
        Type: general
      – SubjectFull: Male
        Type: general
      – SubjectFull: Female
        Type: general
      – SubjectFull: Support Vector Machine
        Type: general
      – SubjectFull: Aged, 80 and over
        Type: general
      – SubjectFull: Case-Control Studies
        Type: general
      – SubjectFull: Sensitivity and Specificity
        Type: general
      – SubjectFull: Middle Aged
        Type: general
      – SubjectFull: Cognitive Dysfunction diagnosis
        Type: general
      – SubjectFull: Cognitive Dysfunction physiopathology
        Type: general
      – SubjectFull: Cognitive Dysfunction classification
        Type: general
      – SubjectFull: Amnesia physiopathology
        Type: general
      – SubjectFull: Amnesia diagnosis
        Type: general
      – SubjectFull: Evoked Potentials, Somatosensory physiology
        Type: general
      – SubjectFull: Evoked Potentials physiology
        Type: general
    Titles:
      – TitleFull: Neural features underlying high-accuracy classification of amnestic mild cognitive impairment using multi-sensory-evoked potentials.
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            – D: 01
              M: 04
              Text: 2026 Apr
              Type: published
              Y: 2026
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            – Type: issn-electronic
              Value: 2509-2723
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