In response to NIMH Strategic priorities “to map the connectomes for mental illness, harness the power of data” and “develop computational approaches” and the Notice of Special Interest (NOT-MH-21-175) regarding the “Use of Human Connectome Data for Secondary Analysis” we leverage data across four Connectomes Related to Human Disease (CRHD) projects, as well as the Human Connectome Project (HCP) Aging and Development Lifespan and the Adolescent Brain Cognitive Development (ABCD) studies to address a major challenge in our field. Specifically, we use novel computational approaches to identify cohesive symptom/cognitive dimensions and subtypes across the continuum of anxious misery disorders in relation to the natural heterogeneity of brain network alterations. This proposal capitalizes on an established record of collaboration between independent CRHD projects and the HCP data core. Multimodal magnetic resonance imaging (MRI), clinical and cognitive data will be integrated for 2,187 people, including 531 adults and 150 adolescent patients with anxious misery disorders, and 1,506 matched healthy people. Cutting edge HCP and UK Biobank processing streams will centrally process data to derive harmonized multimodal imaging features or phenotypes (IDPs) across datasets, extracted from resting state functional MRI, task-derived functional MRI, structural MRI and diffusion imaging data for use in analyses and for dissemination with the scientific community (Aim 1). Using a novel group regularized canonical correlation analysis (GRCCA), we will evaluate the covariation between IDPs and clinical and cognitive measures to identify brain network-symptom/cognitive dimensions of anxious misery across adolescents and adults (Aim 2). In tandem, we will use the unsupervised Uniform Manifold Approximation and Projection (UMAP) with Density-based Spatial Clustering of Applications with Noise (DBSCAN) applied to identify anxious misery subtypes (Aim 3) distinguished by brain network IDP profiles and symptom and cognitive measures. Both data-driven analysis approaches will be applied to determine similarities of brain network- symptom/cognitive dimensions and subtypes across adolescent and adults and their influence on antidepressant treatment outcomes. Our preliminary data suggest these methods will advance our understanding of the links between brain network dysfunction and specific psychopathology across age and in relation to antidepressant response well beyond DSM diagnoses. Public Health Significance: Successful completion of this project will deconstruct and validate the natural heterogeneity of brain circuit alterations underlying transdiagnostic anxious misery disorders. The resulting brain- clinical phenotypes will yield a robust set of dimensional and subtype targets for future clinical and mechanistic investigations of heterogeneity in anxious misery disorders across the lifespan. These phenotypes can also be used to inform precision medicine approaches to individualizing mechanistic and novel treatment studies.

PROJECT NARRATIVE In response to NOSI (NOT-MH-21-175) this proposal leverages multiple large datasets and sophisticated machine learning approaches to address how symptoms of mood, anxiety, and stress, collectively characterized as `anxious misery,' relate to the altered integration of functional brain regions (or networks) across adolescence and adulthood and in relation to treatment response. Multimodal neuroimaging and clinical and cognitive data collected from four Connectomes Related to Human Disease (CRHD) projects, the Human Connectome (HCP) Aging and Development Lifespan projects, and the Adolescent Brain Cognitive Development (ABCD) projects, including 2,187 people, will be analyzed in a unified manner with data-driven methods to understand the relationships between symptoms and brain networks across individuals. Project outcomes will provide a more unified understanding of brain dysfunction in people with different types of anxious misery symptoms and are expected to guide future efforts to provide more effective treatments tailored to specific people.