Abstract
BACKGROUND: Imaging research has not yet delivered reliable psychiatric biomarkers. One challenge, particularly among youth, is high comorbidity. This challenge might be met through canonical correlation analysis designed to model mutual dependencies between symptom dimensions and neural measures. We mapped the multivariate associations that intrinsic functional connectivity manifests with pediatric symptoms of anxiety, irritability, and attention-deficit/hyperactivity disorder (ADHD) as common, impactful, co-occurring problems. We evaluate the replicability of such latent dimensions in an independent sample. METHODS: We obtained ratings of anxiety, irritability, and ADHD, and 10 minutes of resting-state functional magnetic resonance imaging data, from two independent cohorts. Both cohorts (discovery: n = 182; replication: n = 326) included treatment-seeking youth with anxiety disorders, with disruptive mood dysregulation disorder, with ADHD, or without psychopathology. Functional connectivity was modeled as partial correlations among 216 brain areas. Using canonical correlation analysis and independent component analysis jointly we sought maximally correlated, maximally interpretable latent dimensions of brain connectivity and clinical symptoms. RESULTS: We identified seven canonical variates in the discovery and five in the replication cohort. Of these canonical variates, three exhibited similarities across datasets: two variates consistently captured shared aspects of irritability, ADHD, and anxiety, while the third was specific to anxiety. Across cohorts, canonical variates did not relate to specific resting-state networks but comprised edges interconnecting established networks within and across both hemispheres. CONCLUSIONS: Findings revealed two replicable types of clinical variates, one related to multiple symptom dimensions and a second relatively specific to anxiety. Both types involved a multitude of broadly distributed, weak brain connections as opposed to strong connections encompassing known resting-state networks.