Associations between the brain and psychiatric symptoms are likely to be small [1]. Detecting these associations reliably requires large sample sizes, beyond the reach of single research sites. Traditionally, meta-analyses of published imaging studies have been used to aggregate data. However, these approaches are vulnerable to publication bias and synthesize heterogeneous group-level differences that achieve significance in small, underpowered samples, which are susceptible to both type-I and type-II errors [1, 2].

A potentially more robust approach to data aggregation is neuroimaging mega-analysis, initially popularized by the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) consortium [3]. This method involves the uniform processing and integration of individual participant-level data across multiple datasets, containing thousands of participants. Initial work from ENIGMA reported smaller subcortical volumes and cortical surface area in youth with ADHD compared with controls [3]. We have applied such mega-analytic methods to data from large publicly available datasets, resulting in a combined dataset of ~9000 youth with or without Attention-Deficit/Hyperactivity Disorder (ADHD). Our findings support prevailing neurobiological models, revealing greater functional connectivity between the default mode network and task-positive networks [1]; greater functional connectivity within subcortico-cortical circuits centered on the caudate [2]; and less fractional anisotropy within long association white matter tracts [4], in youth with ADHD compared to unaffected controls. Similar associations were found with ADHD-traits in the broader population [1, 2, 4].