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Treffer: Revisiting drunk driving risk among individuals with alcohol use disorder using unsupervised learning: From clinical characteristics and neuropsychological performance to EEG data.

Title:
Revisiting drunk driving risk among individuals with alcohol use disorder using unsupervised learning: From clinical characteristics and neuropsychological performance to EEG data.
Authors:
Su YR; Clinical Psychology Center, Asia University Hospital, Taichung, Taiwan., Liu YL; Taoyuan Psychiatric Center Ministry of Health Welfare, Taoyuan, Taiwan., Sun CK; Department of Emergency Medicine, E-Da Dachang Hospital, I-Shou University, Kaohsiung City, Taiwan; School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan., Yeh PY; Clinical Psychology Center, Asia University Hospital, Taichung, Taiwan; Department of Psychology, College of Medical and Health Science, Asia University, Taichung, Taiwan. Electronic address: PsyYPY@asia.edu.tw., Liu CP; Funheart Psychotherapy Clinic, Taoyuan, Taiwan.
Source:
Journal of affective disorders [J Affect Disord] 2026 Feb 15; Vol. 395 (Pt A), pp. 120718. Date of Electronic Publication: 2025 Nov 17.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7906073 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2517 (Electronic) Linking ISSN: 01650327 NLM ISO Abbreviation: J Affect Disord Subsets: MEDLINE
Imprint Name(s):
Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press.
MeSH Terms:
Alcoholism*/physiopathology , Alcoholism*/psychology , Driving Under the Influence*/psychology , Automobile Driving*/psychology , Electroencephalography* , Unsupervised Machine Learning*, Humans ; Male ; Female ; Adult ; Neuropsychological Tests ; Middle Aged ; Principal Component Analysis
Contributed Indexing:
Keywords: Alcohol use disorder; Driving under the influence; EEG coherence; Machine learning; Unsupervised learning
Entry Date(s):
Date Created: 20251119 Date Completed: 20251208 Latest Revision: 20251208
Update Code:
20260130
DOI:
10.1016/j.jad.2025.120718
PMID:
41260368
Database:
MEDLINE

Weitere Informationen

The strong association between alcohol use disorder (AUD) and driving under the influence of alcohol (DUIA) suggests substantial overlaps across the behavioral, cognitive, and neurobiological domains. Taking advantage of the unsupervised machine learning approach in uncovering hidden patterns, this study incorporated diverse clinical, neuropsychological, and electroencephalographic (EEG) features to identify distinctive patterns among twenty-seven AUD adults with and without DUIA (16 DUIA vs. 11 non-DUIA) recruited from a single tertiary referral center. Following Principal Component Analysis (PCA), K-means clustering was applied to the PCA-transformed feature space based on 457 characteristics, including demographic (e.g., gender and marriage), clinical data (i.e., drinking frequency, depression severity, and emotion), and performance in neuropsychological tests (i.e., the traffic-themed version of the stop signal task, delay discounting task, Iowa gambling test, and community mental status examination), as well as EEG data obtained in resting state and virtual traffic scenarios. The silhouette analysis revealed a peak of clustering performance at a score of 0.479 when k = 9, indicating that the nine-cluster solution provided the optimal balance between compactness and separation. PCA identified beta-band synchronization in the fronto-parietal region as the primary pattern of EEG coherence. Based on the nine-cluster solution, the top five discriminative features within each cluster were predominantly associated with performance indices from the Iowa gambling task and patterns of EEG coherence. The findings highlighted the combined contribution of behavioral decision-making and neural synchronization to DUIA in AUD individuals, reflecting the interplay between cognitive control and fronto-parietal connectivity, consistent with previous empirical evidence.
(Copyright © 2025 Elsevier B.V. All rights reserved.)

Declaration of competing interest This work was supported by the Ministry of Health and Welfare, Taiwan (Grant No. MOHW-YFH-11172). The authors declare that they have no conflicts of interest and financial support.