PROJECT SUMMARY Increased intraindividual variability (IIV) in task performance is a robust symptom of attention deficit/hyperactivity disorder (ADHD). However, while theoretical frameworks suggest inefficient neural processing as a potential cause of IIV, evidence is limited because IIV is typically measured using summary statistics of behavioral outcomes, such as response time variability (RTV). Not only are such measures limited by sparse temporal sampling, but they also aggregate time, ignoring underlying dynamics, and limiting the specificity and thus the translational value of IIV in ADHD. In this project, we propose that neurophysiological measures of neural processing efficiency can be continuously derived from the oscillatory and signal properties of EEG, to track the contribution of top-down signals (low-frequency power), network efficiency (low-frequency oscillatory small-world index) and network interaction stability (signal complexity). These measures can be used as a continuous, within- subject neurophysiological index of neural processing efficiency, and one that bridges summary statistics derived from reaction times and underlying network dynamics. To test this idea, we revisit three existing datasets (n=514) that include children and adults, with and without ADHD, and that contain EEG and concurrent EEG and fMRI, collected during sustained attention tasks. In each dataset, we compute continuous measures of neural efficiency based on EEG signals to, in Aim 1, differentiate between alternate neurophysiological profiles and mechanisms of IIV in ADHD and test if these predict performance outcomes and individual differences in symptoms. In Aim 2, we additionally test if neural efficiency measures predict, within-subject, aberrant interactions between core attention networks and those previously associated with ADHD – namely fronto-parietal, default-mode, ventral/dorsal attention, visual and fronto-striatal. The goal of the present work is to establish the neurophysiological basis of IIV in ADHD, and thus speak to putative clinical targets of IIV, differentiate between current theories of IIV, as well as to validate EEG-based neural efficiency as an effective intermediate indicator of underlying network dynamics.