Leveraging computational strategies to disentangle the genetic and neural underpinnings of ADHD and its associated cognitive systems - Impaired higher-order cognition is well-documented in different forms of neuropsychiatric illness, present in
unaffected relatives, often presumed to underlie behavioral symptoms, and associated with functional
outcome. Cognition is thus exceedingly relevant to studies of the etiology and trajectory of psychopathology.
Nonetheless, the relationship between cognitive decrements and specific psychopathological conditions is not
yet resolved. There are particular knowledge gaps in this regard for attention deficit/hyperactivity disorder
(ADHD), one of the most common child psychiatry conditions worldwide. While models of ADHD have long
highlighted executive functions (EF) as driving the behavioral symptoms of the condition, cognition in ADHD is
increasingly acknowledged to be complex. Twin and family studies link aspects of cognition to ADHD risk, but
findings are inconsistent. Also, not all affected youth show EF deficits, and domains separable from EF are
impaired to varying degrees. Moreover, cognitive decrements within and beyond ADHD disrupt academic and
psychosocial functioning and show limited response to pharmacologic treatments that benefit the disorder.
Thus, understanding the overlapping and separable heritable neurobiology of ADHD and its related cognitive
systems has implications for psychopathology models and patient care. In this proposal, we will study these
issues through the lens of the NIMH’s Research Domain Criteria (RDoC) framework, which encourages a
dimensional approach, interrogation of specific transdiagnostic traits, and multi-level links across genetics,
brain systems and behavior. We will also capitalize on new resources in the field, i.e., advances in cognition
genomics, novel computational strategies, and the Adolescent Brain Cognitive Development (ABCD) study.
Our goal is to validate and demarcate the heritable biological underpinnings of specific cognitive
systems that overlap with and extend beyond the ADHD construct. Our aims converge with NIMH PAR-
21-263, which seeks computational studies to validate dimensional constructs represented in the RDoC matrix
in relation to psychopathology. Aim 1 will extract the genetic basis of ADHD-related cognitive constructs
represented in RDoC using samples in the UK Biobank and comprehensively characterize their overlap with
the genetic risk for ADHD using cutting-edge statistical genetics methods. Aim 2 will develop and validate an
efficient, deep learning based, longitudinal neuroimaging processing pipeline that can extend to youth with
diverse racial and ethnic backgrounds, and produce an atlas of brain regions and neural circuitry that underlie
the cognitive constructs and the ADHD dimension. Aim 3 will train machine learning models that can integrate
genetic risk scores, neuroimaging markers, and cognitive and behavioral phenotypes to predict future ADHD
symptoms, cognitive functioning and functional outcomes. Completing these aims will advance models of the
overlapping and distinct neurobiological bases of ADHD and its associated cognitive systems, which, in turn,
will yield opportunities for risk stratification and novel therapeutics.
