PROJECT SUMMARY/ABSTRACT Research on the biological origins of psychopathology has largely focused on isolated levels of analyses and discrete illness categories. Moreover, most in vivo imaging efforts only consider a single point in time— essentially ignoring the temporal variability of behavior. To establish illness etiologies, we must account for diagnostic heterogeneity, longitudinal change, and genetic risks. Emerging evidence from large population- based studies of healthy populations indicates that individual differences in behavior are reflected in variability across the collective set of functional brain connections (functional connectome). These data suggest that the spectra of symptom profiles observed in patient populations may arise through detectable patterns of network function, with the disturbance of individual systems preferentially contributing to domain-specific (e.g., executive, affective, and social), but disorder-general, impairments. Critically, genetic factors influence the functioning of large-scale networks. Spatial patterns of gene transcription can show strong correspondence with network topography, potentially driving comorbidity between symptomatically related disorders. However, while recent work suggests a genetic basis for the macro-scale organization of the cortical sheet, the extent to which local cellular profiles may underpin the functional properties of the brain and contribute to associated symptom profiles remains to be established. To address the disconnect between mechanism and nosology, the NIMH strategic plan calls for a bottom-up reappraisal of psychopathology across multiple levels of analysis; facilitating the study of relationships from genes to neural circuits and networks through behavior, cutting across disorders as traditionally defined. Directly addressing these objectives, the proposed research will link individual variation in functional connectomes with longitudinal changes in symptom profiles across unipolar depression, bipolar disorder, and schizophrenia through the combined application of neuroimaging, behavioral, and genomic methods. Building upon our prior work, we will generate individual-level brain-based predictions of multidimensional symptom profiles, defining clinical subtypes by clustering participants according to distinct patterns of functional connectivity (Aim 1). Second, we will map transdiagnostic functional connectome variability to longitudinal trajectories of clinical presentation, deriving predictive models of temporal changes across symptom profiles (Aim 2). Third, we will investigate the cellular underpinnings of the human cortical connectome across health and disease. In doing so, we will identify the cellular associates of network function, establish their relationship to in vivo connectome functioning, and assess co-heritability with illness risk (Aim 3). Linking functional connectomes to individual differences in symptom expression, longitudinal changes in clinically relevant behaviors, and associated cellular and genetic factors represents a tremendous opportunity for the field. The proposed project will enable future advances in our understanding of pathogenesis of affective and psychotic illnesses.

PROJECT NARRATIVE Mounting evidence indicates that unipolar depression, bipolar depression, and schizophrenia are marked by abnormalities in functional brain networks, yet these observations have failed to yield clinically useful biomarkers of an individuals' current symptoms or illness risk. To address this pressing need, we propose to identify cellular and genetic contributors to the functioning of large-scale brain networks and characterize their relationship to longitudinal trajectories of clinical presentation in patients with affective and psychotic illnesses. This work translates cutting-edge neuroimaging, data science, and genomic approaches to the clinical arena, developing models that can predict individuals' clinical symptoms from their patterns of functional brain connectivity and identifying the associated genetic and cellular mechanisms of network function in health and disease.