PT  - JOURNAL ARTICLE
AU  - Alexander S. Weigard
AU  - Sarah J. Brislin
AU  - Lora M. Cope
AU  - Jillian E. Hardee
AU  - Meghan E. Martz
AU  - Alexander Ly
AU  - Robert A. Zucker
AU  - Chandra Sripada
AU  - Mary M. Heitzeg
TI  - Evidence accumulation and associated error-related brain activity as computationally informed prospective predictors of substance use in emerging adulthood
AID  - 10.1101/2020.03.06.981035
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.03.06.981035
4099  - http://biorxiv.org/content/early/2020/03/08/2020.03.06.981035.short
4100  - http://biorxiv.org/content/early/2020/03/08/2020.03.06.981035.full
AB  - Substance use peaks during the developmental period known as emerging adulthood (roughly ages 18–25), but not every individual who uses substances during this period engages in frequent or problematic use. Previous studies have suggested that individual differences in neurocognition may prospectively predict problematic substance use, but mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have not yet been identified. Here we take an approach rooted in computational psychiatry, an emerging field in which formal models of neurocognition are used to identify candidate biobehavioral dimensions that confer risk for psychopathology. Specifically, we test whether lower efficiency of evidence accumulation (EEA), a computationally tractable process that drives neurocognitive performance across many tasks, is a risk factor for substance use in emerging adults. In an fMRI substudy within a sociobehavioral longitudinal study (n=106), we find that lower EEA and reductions in a robust neural-level correlate of EEA (error-related activations in salience network and parietal structures) measured at ages 18–21 are both prospectively related to higher levels of substance use during ages 22–26, even after adjusting for other well-known risk factors. Results from Bayesian model comparisons corroborated inferences from conventional hypothesis testing and provided evidence that both EEA and its neural correlates contain unique predictive information about substance use involvement. Overall, these findings suggest that EEA is a mechanistic, computationally tractable neurocognitive risk factor for substance use at a critical developmental period, with clear links to both neural correlates and well-established formal theories of brain function.