Attention-Deficit Hyperactivity Disorder (ADHD), one of the most prevalent child psychiatric disorders, is associated with significant long-term impairment. Despite extensive research, the causes and brain basis of ADHD remain poorly understood. Attempts to delineate `core deficits' in ADHD have remained elusive, in part because of the heterogeneous nature of the disorder but also because of relative weaknesses in methods so far used to characterize cognitive function in children with ADHD. We propose a new EEG source imaging approach that identifies independent sources of EEG information in identifiable cortical areas and permits highly time-resolved network analysis, both at the group and individual levels. We will apply this approach to a large set of NIMH-funded existing EEG and behavioral data collected from children with and without ADHD. Our goal is to develop effective biomarkers that can both improve ADHD diagnosis and to advance the broader NIMH goal of better understanding ADHD pathophysiology at a non-categorical, individual subject level by identifying the position occupied by each ADHD subject in a broad landscape of individual differences linking brain function and symptomology. This will be possibly the most comprehensive look to date at EEG cortical network activation during cognitive performance in children. This comprehensive assessment, with near- millisecond time resolution, in a large sample will clarify the mechanisms underlying cognitive deficits in ADHD. The results will test and demonstrate the ability of emerging EEG source imaging to better characterize individual and group differences in brain and behavior. If successful, this new approach may enable more sensitive diagnosis, individualized treatment, and treatment monitoring for ADHD, and could be applied to study of other psychiatric pathologies, both by mining large existing but still under-exploited EEG data sets and by informing new study designs and analyses.
This research aims to build a new framework for understanding the relationship between brain function and symptoms in child ADHD, by analyzing an existing high-quality EEG/behavioral dataset using innovative analysis tools. These tools will provide a first description of dynamic interactions between brain EEG source areas during cognitive control tasks, as well as reveal connections between brain and behavior that may enable new low-cost approaches to understanding the brain basis of ADHD and for prescription and evaluation of ADHD treatment interventions.
