Citation

Park SH, Zhang Y, Kwon D, Zhao Q, Zahr NM, Pfefferbaum A, Sullivan EV, Pohl KM. Sci. Rep. 2018; 8(1): e8297.

Affiliation

Center for Health Sciences, SRI International, Menlo Park, CA, 94025, USA. kilian.pohl@sri.com.

Copyright

(Copyright © 2018, Nature Publishing Group)

DOI

10.1038/s41598-018-26627-7

PMID

29844507

Abstract

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.

Language: en