Neural Substrates of Autism and ADHD: Reward Circuitry Connectivity and Individual Differences

PROJECT SUMMARY/ABSTRACT Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) exhibit significant clinical overlap, but the shared and distinct biology of these conditions remains incompletely understood. Both ASD and ADHD have been linked in part to the brain’s reward system, with prior work demonstrating that these conditions

are associated with altered function and structure of individual reward structures. However, reward areas do not act in isolation but instead communicate extensively with many higher- and lower-order brain regions, including sensorimotor and cognitive control areas implicated in ASD and ADHD, respectively. To inform our

understanding of the convergent and divergent neural mechanisms underlying ASD and ADHD, there is thus a crucial need to understand how the connectivity patterns of reward structures are altered in these conditions. The proposed project will use cutting-edge resting-state functional magnetic resonance imaging (rsfMRI)

methods together with refined machine learning and statistical modeling approaches to investigate reward functional connectivity in ASD and ADHD. Analyses will consider both categorical diagnoses (ASD, ADHD, comorbid ASD+ADHD, neurotypical controls) and continuous ASD and ADHD symptoms across the population.

Datasets will include the largest available samples in the world for both categorical diagnoses (N>6,000; ages 5-65) and continuous symptoms (N>11,000; ages 9-14). The aims of this project are as follows: Aim 1 will investigate the functional connectivity patterns of reward regions in ASD and ADHD using machine learning and

multivariate statistical approaches. Aim 2 will assess the moderating impact of key sources of heterogeneity (sex, puberty, medication) on reward functional connectivity in ASD and ADHD using machine learning. Aim 3 will create normative reference curves of reward functional connectivity using data-driven modeling approaches

and examine deviations from typical maturational trajectories in ASD and ADHD. Taken together, this work will substantially improve our understanding of reward circuitry in ASD and ADHD, as well as the shared and distinct neural underpinnings of these conditions. In the long-term, this project will contribute to the development and

personalization of novel biologically-grounded treatments for ASD and ADHD. These studies are in line with the NIMH strategic plan to define the brain mechanisms underlying complex behaviors, and to examine mental

illness trajectories across the lifespan. Additionally, this proposal will provide the PI with significant training in the following new areas: (1) refined machine learning methods, (2) advanced statistical modeling approaches, and (3) cutting-edge functional connectivity methods. This training will be completed under the mentorship of Drs.

Paul Thompson, Jose-Luis Ambite and Vince Calhoun, who are world-renowned experts in their respective fields. As a whole, the proposed research and training will allow the PI to become a productive independent investigator using cutting-edge analytic methods to lead translational research on neurodevelopmental disorders and their

heterogeneity to improve precision medicine approaches.