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Alcohol use effects on adolescent brain development revealed by simultaneously removing confounding factors, identifying morphometric patterns, and classifying individuals
- Alcohol use effects on adolescent brain development revealed by simultaneously removing confounding factors, identifying morphometric patterns, and classifying individuals
- Park, Sang Hyun; Zhang, Yong; Kwon, Dongjin; Zhao, Qingyu; Zahr, Natalie M.; Pfefferbaum, Adolf; Sullivan, Edith V.; Pohl, Kilian M.
- DGIST Authors
- Park, Sang Hyun
- Issue Date
- Scientific Reports, 8
- Article Type
- WHITE-MATTER; ROBUST REGRESSION; DECISION-MAKING; CORPUS-CALLOSUM; CEREBRAL-CORTEX; HEAVY-DRINKING; FMRI DATA; TRAJECTORIES; INTELLIGENCE; CONSUMPTION
- Group analysis of brain magnetic resonance imaging (MRI) metrics frequently employs generalized additive models (GAM) to remove contributions of confounding factors before identifying cohort specific characteristics. For example, the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) used such an approach to identify effects of alcohol misuse on the developing brain. Here, we hypothesized that considering confounding factors before group analysis removes information relevant for distinguishing adolescents with drinking history from those without. To test this hypothesis, we introduce a machine-learning model that identifies cohort-specific, neuromorphometric patterns by simultaneously training a GAM and generic classifier on macrostructural MRI and microstructural diffusion tensor imaging (DTI) metrics and compare it to more traditional group analysis and machine-learning approaches. Using a baseline NCANDA MR dataset (N = 705), the proposed machine learning approach identified a pattern of eight brain regions unique to adolescents who misuse alcohol. Classifying high-drinking adolescents was more accurate with that pattern than using regions identified with alternative approaches. The findings of the joint model approach thus were (1) impartial to confounding factors; (2) relevant to drinking behaviors; and (3) in concurrence with the alcohol literature. © 2018 The Author(s).
- Nature Publishing Group
- Related Researcher
Park, Sang Hyun
Medical Image & Signal Processing Lab
컴퓨터비전, 인공지능, 의료영상처리
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- Department of Robotics EngineeringMedical Image & Signal Processing Lab1. Journal Articles
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