Citation

Squeglia LM, Ball TM, Jacobus J, Brumback T, McKenna BS, Nguyen-Louie TT, Sorg SF, Paulus MP, Tapert SF. Am. J. Psychiatry 2016; 174(2): 172-185.

Affiliation

From the Addiction Sciences Division, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston; the Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif.; the Department of Psychiatry, University of California, San Diego, La Jolla; the VA San Diego Healthcare System, San Diego; the San Diego State University and University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego; and the Laureate Institute for Brain Research, Tulsa, Okla.

Copyright

(Copyright © 2016, American Psychiatric Association)

DOI

10.1176/appi.ajp.2016.15121587

PMID

27539487

Abstract

OBJECTIVE: Underage drinking is widely recognized as a leading public health and social problem for adolescents in the United States. Being able to identify at-risk children before they initiate heavy alcohol use could have immense clinical and public health implications; however, few investigations have explored individual-level precursors of adolescent substance use. This prospective investigation used machine learning with demographic, neurocognitive, and neuroimaging data in substance-naive adolescents to predict alcohol use initiation by age 18.

METHOD: Participants (N=137) were healthy substance-naive adolescents (ages 12-14) who underwent neuropsychological testing and structural and functional magnetic resonance imaging (sMRI and fMRI), and then were followed annually. By age 18, 70 youths (51%) initiated moderate to heavy alcohol use, and 67 remained nonusers. Random forest classification models generated individual alcohol use outcome predictions based on demographic, neuropsychological, sMRI, and fMRI data.

RESULTS: The final random forest model was 74% accurate, with good sensitivity (74%) and specificity (73%). The model contained 34 predictors contributing to alcohol use by age 18, including several demographic and behavioral factors (being male, higher socioeconomic status, early dating, more externalizing behaviors, positive alcohol expectancies), worse executive functioning, and thinner cortices and less brain activation in diffusely distributed regions of the brain. Inclusion of neuropsychological, sMRI, and fMRI data significantly increased the prediction accuracy of the model.

CONCLUSIONS: The results provide evidence that multimodal neuroimaging data, as well as neuropsychological testing, can be used to generate predictions of future behaviors such as adolescent alcohol use with significantly better accuracy than demographic information alone.

Language: en