# Longitudinal Investigation of the Neurobiological Underpinnings of Risk Behavior in ADHD throughout the Adolescent Transition: The Key Role of Cognitive Control and Motivation Network Development

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $525,635

## Abstract

PROJECT SUMMARY/ABSTRACT
Attention-deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed developmental disorder of
childhood, affecting ~9% of children nationwide. Although ADHD dramatically increases the risk for poor
academic achievement, substance abuse, and criminal behavior, particularly in adolescence, too little is known
regarding how neurobiological developmental trajectories underlie these behavioral and clinical outcomes. This
remains the case in spite of the importance of such work for earlier identification of risk factors, more targeted
treatment models, and, in turn, education, juvenile justice, and healthcare savings for individuals, families, and
society. The goal of the current project, therefore, is to characterize longitudinal neural, behavioral, and clinical
trajectories of youth with ADHD from late childhood to mid-adolescence. Brain systems underlying cognitive
control and motivation in particular have been identified as centrally important both to the neural etiology of
ADHD and to the general increase in risk-taking behavior and poor decision-making observed in typically
developing (TD) adolescents. An important aspect of these models is how brain regions underlying these
processes form coherent networks, as well as how these networks interact and influence each other to produce
behavior. Here we bring together these two disparate literatures to gain understanding of the transition to
adolescence in youth with ADHD. Thus, this proposal focuses on the maturational course of the cognitive control
and motivation systems, individually and in interaction, in youth with and without ADHD in a multi-session
longitudinal design. The aims of this proposal include: 1) Characterize behavioral trajectories of cognitive control,
motivation, and their interaction in ADHD and TD youth from childhood into adolescence; 2) Characterize the
development of structural and functional brain network organization during the same time period, focusing on
brain networks underlying cognitive control and motivation; and 3) Identify neural, behavioral, and clinical
features of pre-adolescent ADHD that predict clinical outcomes and risk-taking behavior during adolescence. To
address these aims, innovative network analytic tools based on graph theory and structural equation modeling
will be applied to structural and functional connectivity estimates of MRI data during diffusion-weighted imaging
(structural) and during rest, cognitive control, motivation, and risk-taking tasks (functional). These techniques are
uniquely able to simultaneously characterize the strength and coherence of within-network structural/functional
connectivity and across-network interactions, as well as to identify important brain network features that
differentiate across groups. Additionally, behavioral performance on the cognitive control, motivation and risk-
taking tasks, ADHD symptomatology, and risk-taking attitudes and behavior will be assessed. This program o...

## Key facts

- **NIH application ID:** 10189700
- **Project number:** 5R01MH119091-03
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Jessica R Cohen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $525,635
- **Award type:** 5
- **Project period:** 2019-06-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10189700

## Citation

> US National Institutes of Health, RePORTER application 10189700, Longitudinal Investigation of the Neurobiological Underpinnings of Risk Behavior in ADHD throughout the Adolescent Transition: The Key Role of Cognitive Control and Motivation Network Development (5R01MH119091-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10189700. Licensed CC0.

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