Academic Journal
Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach.
| Τίτλος: | Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach. |
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| 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 |
| ISSN: | 1872-8952 |
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| DOI: | 10.1016/j.clinph.2026.2111917 |