Date of Award
Master of Arts (MA)
Predicting teenage drug use is key to understanding the etiology of substance abuse. However, classic predictive modeling procedures are prone to overfitting and fail to generalize to independent observations. To mitigate these concerns, cross-validated logistic regression with elastic-net regularization was used to predict cannabis use by age 16 from a large sample of fourteen year olds (N=1,319). High-dimensional data (p = 2,413) including parent and child psychometric data, child structural and functional MRI data, and genetic data (candidate single-nucleotide polymorphisms, "SNPs") collected at age 14 were used to predict the initiation of cannabis use (minimum six occasions) by age 16. Analyses were conducted separately for males and females to uncover sex-specific predictive profiles. The performance of the predictive models were assessed using the area under the receiver-operating characteristic curve ("ROC AUC"). Final models returned high predictive performance (generalization mean ROC AUCmales=.71, mean ROC AUCfemales=.81) and contained psychometric features common to both sexes. These common psychometric predictors included greater stressful life events, novelty-seeking personality traits of both the parent and child, and parental cannabis use. In contrast, males exhibited distinct functional neurobiological predictors related to a response- inhibition fMRI task, whereas females exhibited distinct neurobiological predictors related to a social processing fMRI task. Furthermore, the brain predictors exhibited sex- specific effects as the brain predictors of cannabis use for one sex failed to predict cannabis use for the opposite sex. These sex-specific brain predictors also exhibited drug- specific effects as they failed to predict binge-drinking by age 16 in an independent sample of youths. When collapsed across sex, a gene-specific analysis suggested that opioid receptor genetic variation also predicted cannabis use by age 16. Two SNPs on the gene coding for the primary mu-opioid receptor exhibited genetic risk effects, while one SNP on the gene coding for the primary delta-opioid receptor exhibited genetic protective effects. Taken together, these results demonstrate that adolescent cannabis use is reliably predicted in males and females from shared and unique biobehavioral features. These analyses also underscore the need for refined predictive modeling procedures as well as sex-specific inquiries into the etiology of substance abuse. The sex-specific risk-profiles uncovered from these analyses might inform potential etiological mechanisms contributing to substance abuse in adolescence as all predictors were measured prior to the onset of cannabis use.
Number of Pages
Spechler, Philip, "Predictive Modeling of Adolescent Cannabis Use From Multimodal Data" (2017). Graduate College Dissertations and Theses. 690.