# 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 · 2022 · $96,914

## 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 despite 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 main goal of the parent grant, “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” (R01MH119091), is to examine longitudinal neural, behavioral, and clinical trajectories of
youth with ADHD from late childhood to mid-adolescence. We focus on neurobehavioral processes related to
cognitive control, or executive function (EF), and motivation, as they have been identified as centrally important
both to the etiology of ADHD and to the general increase in risk-taking behavior observed in typically developing
(TD) adolescents. Notably, there is marked heterogeneity in terms of EF difficulties experienced by youth with
ADHD. Youth with ADHD who experience EF difficulties are at higher risk of poorer outcomes than those without
EF difficulties, regardless of severity of ADHD symptoms. Moreover, different components of EF differentially
relate to outcomes. Specifically, youth with ADHD with deficits in updating have poor emotion regulation, while
youth with ADHD with deficits in shifting have low academic achievement. Therefore, it is crucial to characterize
longitudinal trajectories of distinct components of EF to identify youth with ADHD who are more at risk for future
negative outcomes. Thus, in this supplement we propose to more thoroughly assess EF and to longitudinally
characterize trajectories of functional brain network organization and multiple distinct EF components throughout
the transition to adolescence in youth with ADHD and TD youth. We additionally propose to identify how these
trajectories relate to adolescent outcomes. We achieve this by proposing an additional behavioral testing session
for participants already enrolled in the parent study in which we administer a battery of EF tasks and assess
academic achievement-related outcomes. By combining this new data collection with brain imaging, self-report,
and parent-report data collected through the parent grant, we will achieve the following aims: 1) Characterize
behavioral trajectories of distinct components of EF in ADHD throughout the transition to adolescence; 2)
Characterize the relationship between trajectories of functional brain network organization and EF i...

## Key facts

- **NIH application ID:** 10597855
- **Project number:** 3R01MH119091-03S1
- **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:** 2022
- **Award amount:** $96,914
- **Award type:** 3
- **Project period:** 2022-07-28 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10597855

## Citation

> US National Institutes of Health, RePORTER application 10597855, 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 (3R01MH119091-03S1). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10597855. Licensed CC0.

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