Academic Journal
Προβολή σε EDS

Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach.
Συγγραφείς: Song YW; Department of Applied Artificial Intelligence, Hanyang University, Ansan, the Republic of Korea., Park YH; Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, the Republic of Korea., Kim S; Department of Applied Artificial Intelligence, Hanyang University, Ansan, the Republic of Korea., Kim E; Department of Human-Computer Interaction, Hanyang University, Ansan, the Republic of Korea., Kim S; Department of Applied Artificial Intelligence, Hanyang University, Ansan, the Republic of Korea; Department of Human-Computer Interaction, Hanyang University, Ansan, the Republic of Korea. Electronic address: sdm04sdm@hanmail.net., Lee KH; Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju, the Republic of Korea; Department of Biomedical Sciences, Chosun University, Gwangju, the Republic of Korea; Korea Brain Research Institute, Daegu, the Republic of Korea. Electronic address: leekho@chosun.ac.kr.
Πηγή: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology [Clin Neurophysiol] 2026 Aug; Vol. 188, pp. 2111917. Date of Electronic Publication: 2026 May 06.
Τύπος έκδοσης: Comparative Study; Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 100883319 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8952 (Electronic) Linking ISSN: 13882457 NLM ISO Abbreviation: Clin Neurophysiol Subsets: MEDLINE
Imprint Name(s): Original Publication: Amsterdam : Elsevier, c1999-
Ιατρικοί όροι (MeSH): Electroencephalography*/classification , Electroencephalography*/instrumentation , Electroencephalography*/methods , Evoked Potentials*/physiology , Magnetic Resonance Imaging*/classification , Magnetic Resonance Imaging*/methods , Cognitive Dysfunction*/diagnosis , Cognitive Dysfunction*/etiology , Machine Learning* , Wearable Electronic Devices*, Biomarkers/analysis ; Plaque, Amyloid/complications ; Plaque, Amyloid/psychology ; Aging in Place/psychology ; Case-Control Studies ; Positron-Emission Tomography ; Humans ; Male ; Female ; Aged ; Aged, 80 and over
Περίληψη: Objective: Mild cognitive impairment (MCI) is an intermediary stage between typical cognitive aging and dementia. Identifying reliable biomarkers for early detection of MCI is crucial for slowing disease progression. This study explored multimodal biomarkers associated with amyloid-positive MCI and assessed wearable EEG/ERP and MRI features using machine learning.
Methods: This study included 70 patients with MCI with amyloid-positive positron emission tomography (PET) scans and 140 cognitively normal (CN) individuals with negative PET scans. Data were collected using prefrontal wearable EEG/ERP and MRI devices. EEG data were obtained in three paradigms: resting-state, sensory-evoked potentials, and selective attention tasks. Machine learning techniques were applied to classify MCI and CN groups using these biomarkers.
Results: EEG/ERP analysis suggested differences in frontal alpha asymmetry, power ratio, and ERP components between the MCI and CN groups. MRI revealed atrophy of the hippocampus and accumbens along with cortical thinning in specific brain regions. Combining EEG/ERP and MRI features in a machine learning model yielded the highest classification accuracy of 80.90%.
Conclusions: Prefrontal wearable EEG/ERP and MRI offered complementary candidate biomarkers of MCI in community-dwelling individuals using a machine learning approach.
Significance: These findings support further validation of EEG/ERP and MRI biomarkers in larger and externally validated cohorts.
(Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Biomarkers; Cognitive decline; EEG; MCI; MRI; Machine learning
Substance Nomenclature: 0 (Biomarkers)
Entry Date(s): Date Created: 20260517 Date Completed: 20260701 Latest Revision: 20260701
Update Code: 20260701
DOI: 10.1016/j.clinph.2026.2111917
PMID: 42143838
Βάση Δεδομένων: MEDLINE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://www.doi.org/10.1016/j.clinph.2026.2111917?
    Name: ScienceDirect (all content) (s7799221)
    Category: fullText
    Text: View record from ScienceDirect
    MouseOverText: View record from ScienceDirect
  – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:cmedm&genre=article&issn=18728952&ISBN=&volume=188&issue=&date=20260801&spage=2111917&pages=2111917&title=Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology&atitle=Combining%20EEG%2C%20event-related%20potentials%2C%20and%20MRI%20biomarkers%20for%20detection%20of%20mild%20cognitive%20impairment%3A%20A%20machine%20learning%20approach.&aulast=Song%20YW&id=DOI:10.1016/j.clinph.2026.2111917
    Name: Full Text Finder (for New FTF UI) (ns324271)
    Category: fullText
    Text: Full Text Finder
    MouseOverText: Full Text Finder
Header DbId: cmedm
DbLabel: MEDLINE
An: 42143838
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Song+YW%22">Song YW</searchLink>; Department of Applied Artificial Intelligence, Hanyang University, Ansan, the Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Park+YH%22">Park YH</searchLink>; Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, the Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+S%22">Kim S</searchLink>; Department of Applied Artificial Intelligence, Hanyang University, Ansan, the Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+E%22">Kim E</searchLink>; Department of Human-Computer Interaction, Hanyang University, Ansan, the Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+S%22">Kim S</searchLink>; Department of Applied Artificial Intelligence, Hanyang University, Ansan, the Republic of Korea; Department of Human-Computer Interaction, Hanyang University, Ansan, the Republic of Korea. Electronic address: sdm04sdm@hanmail.net.<br /><searchLink fieldCode="AU" term="%22Lee+KH%22">Lee KH</searchLink>; Gwangju Alzheimer's and Related Dementia Cohort Research Center, Chosun University, Gwangju, the Republic of Korea; Department of Biomedical Sciences, Chosun University, Gwangju, the Republic of Korea; Korea Brain Research Institute, Daegu, the Republic of Korea. Electronic address: leekho@chosun.ac.kr.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22100883319%22">Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology</searchLink> [Clin Neurophysiol] 2026 Aug; Vol. 188, pp. 2111917. <i>Date of Electronic Publication: </i>2026 May 06.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Comparative Study; Journal Article
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier%22">Elsevier </searchLink><i>Country of Publication: </i>Netherlands <i>NLM ID: </i>100883319 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1872-8952 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2213882457%22">13882457 </searchLink><i>NLM ISO Abbreviation: </i>Clin Neurophysiol <i>Subsets: </i>MEDLINE
– Name: PublisherInfo
  Label: Imprint Name(s)
  Group: PubInfo
  Data: <i>Original Publication</i>: Amsterdam : Elsevier, c1999-
– Name: SubjectMESH
  Label: MeSH Terms
  Group: Su
  Data: <searchLink fieldCode="MM" term="%22Electroencephalography%22">Electroencephalography*</searchLink>/<searchLink fieldCode="MM" term="%22Electroencephalography+classification%22">classification</searchLink> <br /><searchLink fieldCode="MM" term="%22Electroencephalography%22">Electroencephalography*</searchLink>/<searchLink fieldCode="MM" term="%22Electroencephalography+instrumentation%22">instrumentation</searchLink> <br /><searchLink fieldCode="MM" term="%22Electroencephalography%22">Electroencephalography*</searchLink>/<searchLink fieldCode="MM" term="%22Electroencephalography+methods%22">methods</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="MM" term="%22Magnetic+Resonance+Imaging%22">Magnetic Resonance Imaging*</searchLink>/<searchLink fieldCode="MM" term="%22Magnetic+Resonance+Imaging+classification%22">classification</searchLink> <br /><searchLink fieldCode="MM" term="%22Magnetic+Resonance+Imaging%22">Magnetic Resonance Imaging*</searchLink>/<searchLink fieldCode="MM" term="%22Magnetic+Resonance+Imaging+methods%22">methods</searchLink> <br /><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+etiology%22">etiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Machine+Learning%22">Machine Learning*</searchLink> <br /><searchLink fieldCode="MM" term="%22Wearable+Electronic+Devices%22">Wearable Electronic Devices*</searchLink><br /><searchLink fieldCode="MH" term="%22Biomarkers%22">Biomarkers</searchLink>/<searchLink fieldCode="MH" term="%22Biomarkers+analysis%22">analysis</searchLink> ; <searchLink fieldCode="MH" term="%22Plaque%2C+Amyloid%22">Plaque, Amyloid</searchLink>/<searchLink fieldCode="MH" term="%22Plaque%2C+Amyloid+complications%22">complications</searchLink> ; <searchLink fieldCode="MH" term="%22Plaque%2C+Amyloid%22">Plaque, Amyloid</searchLink>/<searchLink fieldCode="MH" term="%22Plaque%2C+Amyloid+psychology%22">psychology</searchLink> ; <searchLink fieldCode="MH" term="%22Aging+in+Place%22">Aging in Place</searchLink>/<searchLink fieldCode="MH" term="%22Aging+in+Place+psychology%22">psychology</searchLink> ; <searchLink fieldCode="MH" term="%22Case-Control+Studies%22">Case-Control Studies</searchLink> ; <searchLink fieldCode="MH" term="%22Positron-Emission+Tomography%22">Positron-Emission Tomography</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Aged%22">Aged</searchLink> ; <searchLink fieldCode="MH" term="%22Aged%2C+80+and+over%22">Aged, 80 and over</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: Mild cognitive impairment (MCI) is an intermediary stage between typical cognitive aging and dementia. Identifying reliable biomarkers for early detection of MCI is crucial for slowing disease progression. This study explored multimodal biomarkers associated with amyloid-positive MCI and assessed wearable EEG/ERP and MRI features using machine learning.<br />Methods: This study included 70 patients with MCI with amyloid-positive positron emission tomography (PET) scans and 140 cognitively normal (CN) individuals with negative PET scans. Data were collected using prefrontal wearable EEG/ERP and MRI devices. EEG data were obtained in three paradigms: resting-state, sensory-evoked potentials, and selective attention tasks. Machine learning techniques were applied to classify MCI and CN groups using these biomarkers.<br />Results: EEG/ERP analysis suggested differences in frontal alpha asymmetry, power ratio, and ERP components between the MCI and CN groups. MRI revealed atrophy of the hippocampus and accumbens along with cortical thinning in specific brain regions. Combining EEG/ERP and MRI features in a machine learning model yielded the highest classification accuracy of 80.90%.<br />Conclusions: Prefrontal wearable EEG/ERP and MRI offered complementary candidate biomarkers of MCI in community-dwelling individuals using a machine learning approach.<br />Significance: These findings support further validation of EEG/ERP and MRI biomarkers in larger and externally validated cohorts.<br /> (Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
– Name: SubjectMinor
  Label: Contributed Indexing
  Group:
  Data: <i>Keywords: </i>Biomarkers; Cognitive decline; EEG; MCI; MRI; Machine learning
– Name: NumberCAS
  Label: Substance Nomenclature
  Group: ID
  Data: 0 (Biomarkers)
– Name: DateEntry
  Label: Entry Date(s)
  Group: Date
  Data: <i>Date Created: </i>20260517 <i>Date Completed: </i>20260701 <i>Latest Revision: </i>20260701
– Name: DateUpdate
  Label: Update Code
  Group: Date
  Data: 20260701
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1016/j.clinph.2026.2111917
– Name: AN
  Label: PMID
  Group: ID
  Data: 42143838
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=42143838
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.clinph.2026.2111917
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 2111917
    Subjects:
      – SubjectFull: Biomarkers analysis
        Type: general
      – SubjectFull: Plaque, Amyloid complications
        Type: general
      – SubjectFull: Plaque, Amyloid psychology
        Type: general
      – SubjectFull: Aging in Place psychology
        Type: general
      – SubjectFull: Case-Control Studies
        Type: general
      – SubjectFull: Positron-Emission Tomography
        Type: general
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Male
        Type: general
      – SubjectFull: Female
        Type: general
      – SubjectFull: Aged
        Type: general
      – SubjectFull: Aged, 80 and over
        Type: general
      – SubjectFull: Electroencephalography classification
        Type: general
      – SubjectFull: Electroencephalography instrumentation
        Type: general
      – SubjectFull: Electroencephalography methods
        Type: general
      – SubjectFull: Evoked Potentials physiology
        Type: general
      – SubjectFull: Magnetic Resonance Imaging classification
        Type: general
      – SubjectFull: Magnetic Resonance Imaging methods
        Type: general
      – SubjectFull: Cognitive Dysfunction diagnosis
        Type: general
      – SubjectFull: Cognitive Dysfunction etiology
        Type: general
      – SubjectFull: Machine Learning
        Type: general
      – SubjectFull: Wearable Electronic Devices
        Type: general
    Titles:
      – TitleFull: Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Song YW
      – PersonEntity:
          Name:
            NameFull: Park YH
      – PersonEntity:
          Name:
            NameFull: Kim S
      – PersonEntity:
          Name:
            NameFull: Kim E
      – PersonEntity:
          Name:
            NameFull: Kim S
      – PersonEntity:
          Name:
            NameFull: Lee KH
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Text: 2026 Aug
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 1872-8952
          Numbering:
            – Type: volume
              Value: 188
          Titles:
            – TitleFull: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
              Type: main
ResultId 1