Autism and attention-deficit/hyperactivity disorder (ADHD) are two common neurodevelopmental conditions, but we still lack a deep understanding of the biological mechanisms that underlie them.
A new grant from the National Institute of Mental Health will provide more than $919,000 to support the USC Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) in a five-year investigation into one key theory of what causes the two conditions: changes in the brain’s reward network.
“We still don’t understand the full extent of the shared and distinct mechanisms of these two conditions, and that’s particularly true for reward system connectivity, which has not been investigated very much in these conditions,” said Katherine Lawrence, PhD, an assistant professor of research neurology at the Keck School of Medicine of USC, a member of the Stevens INI and principal investigator of the study.
Lawrence will use high-resolution functional magnetic resonance imaging (fMRI) scans to analyze how the reward network interacts with other parts of the brain in autism, ADHD and in cases where the two conditions co-occur. She will then apply advanced machine learning and statistical methods to map how that reward connectivity changes across the lifespan and whether it differs based on individual factors, such as sex.
Down the line, the research could support new, personalized interventions for the conditions that are grounded in a sophisticated understanding of how the brain responds to external rewards.
“As a key step along that path, we hope this research can help define which biological pathways are most important to consider in future studies,” Lawrence said.
An unprecedented dataset
The brain’s reward network is a group of structures that control motivation, pleasure and other behaviors that bring satisfaction. It includes the prefrontal cortex, nucleus accumbens, caudate, putamen and other regions.
One key reward theory suggests that autism arises from early-life differences in sensitivity to social cues, such as a caregiver’s face or voice—and that infants who are later diagnosed with autism may find these stimuli less motivating and attention-grabbing, on average, than other infants do. In ADHD, symptoms of inattention and hyperactivity may be closely related to differences in decision-making and motivation that stem from changes in the brain’s reward network.
“We’ll be examining reward connectivity and seeing how reward areas communicate with other parts of the brain – including sensorimotor and cognitive control areas that are also implicated in these conditions,” Lawrence said.
Understanding where the two conditions overlap is another key priority, because people with both ADHD and autism face more challenges, including more elevated symptoms, less response to treatment and greater challenges with independent living.
Lawrence and her team are well-equipped to investigate that uncharted territory. They will leverage data from the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium, a global network of scientists who pool anonymized data on research participants to answer pressing questions about brain-based conditions.
The dataset includes brain scans and other information from more than 3,000 people diagnosed with ADHD or autism, as well as more than 11,000 people from across the population who have had their autism and ADHD traits assessed by researchers through the Adolescent Brain Cognitive Development (ABCD) Study.
This “unprecedented large-scale sample” will help ensure findings are reproducible and generalizable to broader populations, Lawrence said. It will also allow her team to investigate individual differences in experiences of ADHD and autism, including sex differences, effects related to puberty and the role of various medications—a key step toward understanding who is most likely to develop the conditions and how to intervene.
Mapping reward connectivity
Lawrence will start by rigorously mapping the pathways that connect the brain’s reward centers to one another and to other parts of the brain in autism, ADHD and co-occurring autism and ADHD. She will leverage fMRI scans from the ENIGMA network taken from the brain in its “resting state,” when individuals are not completing a specific task.
Next, she will apply advanced machine learning and statistical techniques that allow researchers to study how a series of factors relate to one another, in order to understand how individual differences might predict changes in the reward network.
“For instance, do autistic children and autistic adults show differences in reward connectivity?” Lawrence said.
Finally, she and her team will use data modeling methods to map the expected developmental course of ADHD and autism across the lifespan, akin to pediatric growth charts that are already used by doctors.
“That would also allow future researchers to check, for any individual study participant, where they fall relative to the rest of the population in terms of their reward connectivity,” Lawrence said.
Ultimately, she hopes those insights will guide the development of targeted, personalized interventions for the two conditions.
About this research
This work is support by the National Institute of Mental Health [K01MH135160].