# Connectome-based prediction and neurodevelopmental trajectories of alcohol phenotypes across development

> **NIH NIH R01** · YALE UNIVERSITY · 2021 · $426,735

## Abstract

Abstract
Alcohol initiation at an early age is associated with numerous negative outcomes, including a significant increase
in the risk of developing an alcohol-use disorder later in life. Vulnerability for early misuse and other problematic
alcohol use behaviors have been linked to individual differences in brain function. However, few studies have
sought to identify brain-based predictors (‘neuromarkers’) of alcohol use behaviors in youth. Identification of
brain-based predictors of alcohol use behaviors in youth is essential for the development of more effective early
prevention and intervention efforts. This proposal combines machine learning and longitudinal modeling
approaches to 1) identify neural networks predictive of early alcohol initiation and misuse and 2) chart the
developmental trajectories of these networks over time in a large sample of youth (N>3,000) using data from
three unique, proprietary and completed datasets. Neural networks conferring vulnerability for alcohol use
behaviors during adolescence will be identified using connectome-based predictive modeling (CPM). CPM is a
machine-learning method of generating behavioral predictions from individual patterns of brain organization; i.e.,
functional connectivity matrices. Unlike traditional machine learning approaches, CPM is entirely data-driven and
requires no a priori selection of brain regions or networks. As such, CPM is both a predictive tool and a method
of identifying networks that underlie specific behaviors; i.e., neuromarkers. CPM has been successfully used to
predict complex behaviors including future abstinence and other addiction-relevant phenotypes. This proposal
will use CPM to identify neuromarkers of alcohol initiation and predict transitions to risky drinking in youth (AIM
1). Quantification of changes in brain function, e.g., growth curve trajectory analysis, is central to the
characterization of developmental phenomena. Analyses of developmental trajectories can be used to identify
particularly sensitive growth periods, detect variations that may signal risk, define modifiable targets, and monitor
the impact of environment and interventions on development. While extant data indicate alcohol-related
alterations in neural development, very few studies have assessed interactions between neurodevelopmental
trajectories over time and alcohol-use behaviors. Developmental trajectories of identified networks in relation to
alcohol use behaviors over time will be assessed using multilevel modeling (AIM 2). This proposal represents
the first attempt to identify neural networks predictive of alcohol-initiation and risky drinking using a wholly data-
driven, machine learning approach in a large sample of youth and does so using existing data. This is a critical
step toward identifying a reliable predictor of alcohol initiation in youth and will shed light on individual difference
factors representing vulnerability for misuse. Such predictors are needed to understand th...

## Key facts

- **NIH application ID:** 10124252
- **Project number:** 5R01AA027553-03
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Dustin Scheinost
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $426,735
- **Award type:** 5
- **Project period:** 2019-06-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10124252

## Citation

> US National Institutes of Health, RePORTER application 10124252, Connectome-based prediction and neurodevelopmental trajectories of alcohol phenotypes across development (5R01AA027553-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10124252. Licensed CC0.

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