# Leveraging Bifactor Modeling to Test Prospective Direct and Mediational Effects of Adolescent Alcohol Use and Externalizing Symptoms on the Neurobiological Development of Executive Functioning

> **NIH NIH F31** · STATE UNIVERSITY OF NEW YORK AT BUFFALO · 2022 · $33,982

## Abstract

7. PROJECT SUMMARY/ABSTRACT
Adolescence is a period of substantial brain maturation30. Considering the neurobiological development that
occurs during this period, a central concern to researchers, policy makers, and parents is the impact of
adolescent alcohol use (AU) on cognitive capacities critical to life success, like executive functioning (EF). EF
refers to a family of top-down mental processes that support concentration, attention, and behavioral
regulation44. Healthy development of EF is essential for mental and physical health, academic achievement,
and success in life31,32. It has been proposed that adolescent AU produces physiological and neurobiological
events that derail healthy EF development18,30. However, support for this association has been mixed8,35.
Equivocal support may be due to (1) failure of past work to consider AU in the broader context of externalizing
symptoms and social development33-35, and (2) lack of neuroimaging and behavioral task measures of EF20,36.
Adolescent AU often occurs in a broader context of externalizing symptoms (rule-breaking, aggression) and
other drug use38, and there is evidence that externalizing symptoms disrupt healthy development of EF through
a developmental cascade that involves poor adaptation across multiple contexts (parenting, peers, school)16,17.
Co-occurring drug use may also have direct neurotoxic effects that derail EF7,13,18. Hence, poor EF that has
been attributed to the neurotoxic effects of adolescent AU may actually be due to these other co-occurring
behaviors. Additionally, equivocal support for the AU-EF association may be due to considerable heterogeneity
in measurement of EF36. No ‘‘process-pure’’ measures to assess EF exist102. Therefore, multimethod
approaches are essential102 and theories of addiction assert that it is critically important to use neuroimaging
and task methods to measure EF101. This fellowship seeks to provide clarity to the AU-EF literature. We
propose to (1) distinguish general externalizing symptoms from domain specific symptoms (including AU,
aggression, rule-breaking, drug use) using sophisticated longitudinal bifactor modeling, (2) examine
prospective associations between general externalizing symptoms and domain specific symptoms, and EF
(measured by fMRI and a behavioral task) across adolescence and early adulthood, and (3) examine whether
poor adaptation across several social contexts (parents, peers, and school) mediates associations between
externalizing symptoms and deficits in EF. A high-risk longitudinal sample (N=3,337) of children of alcoholics
from the Michigan Longitudinal Study will be used to test the proposed aims46. Data will include self-reports of
AU, drug use, externalizing symptoms, and social functioning, and an EF behavioral task during fMRI scanning
at four waves spanning early adolescence to early adulthood. Our longitudinal data will allow us to examine
when risk and protective factors to EF may be especially salient, informin...

## Key facts

- **NIH application ID:** 10433877
- **Project number:** 5F31AA028973-02
- **Recipient organization:** STATE UNIVERSITY OF NEW YORK AT BUFFALO
- **Principal Investigator:** Kathleen Joanne Paige Coscia
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $33,982
- **Award type:** 5
- **Project period:** 2021-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10433877

## Citation

> US National Institutes of Health, RePORTER application 10433877, Leveraging Bifactor Modeling to Test Prospective Direct and Mediational Effects of Adolescent Alcohol Use and Externalizing Symptoms on the Neurobiological Development of Executive Functioning (5F31AA028973-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10433877. Licensed CC0.

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