Converging evidence indicates that the boundaries separating nominally distinct psychiatric diagnoses are not sharp or discontinuous with normal behavioral traits and brain function. In healthy populations, 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 transdiagnostic symptom profiles observed in patient populations may arise through detectable patterns of network function. The intrinsic connectivity of the functional connectome is under strong genetic influence. Spatial patterns of gene transcription recapitulate the topography of large-scale brain networks, potentially driving comorbidity between symptomatically related disorders. However, most of what we currently know about the human connectome comes from the study of healthy populations, impeding the development of fully dimensional models of brain function and obscuring the interactions through which genetic and neurobiological variation might coalesce to support suites of behaviors and illness risk within an individual. 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, our proposal will link individual variation in functional connectomes with symptom profiles across unipolar depression, bipolar depression, and schizophrenia through the combined application of neuroimaging, behavioral, and genomic methods. We will establish key biological and clinical features of the functional connectome in three stages. First, we recently established that disruptions within the frontoparietal network (spanning aspects of dorsolateral and dorsomedial prefrontal, lateral parietal, and posterior temporal cortices) reflect a shared feature of schizophrenia and psychotic bipolar disorder. Building upon this work, we will quantify the extent to which frontoparietal connectivity may reflect a disorder-general marker of symptom severity across both affective and psychotic illnesses, validating the key psychological features (Aim 1). Second, we will map transdiagnostic functional connectome variability to the diversity of clinical presentations, extending our analyses across cortex to develop predictive models of multidimensional symptom profiles (Aim 2). Third, we will identify novel patterns of gene expression that follow the spatial organization of large-scale brain networks, establish the impact of contributing loci on in vivo connectome functioning, and assess co-heritability with illness risk (Aim 3). Completion of these aims will yield insights into the neural, behavioral, and genetic basis of affective and psychotic illnesses, providing a crucial step towards the establishment of a new framework for psychiatric classification grounded in etiology and pathogenesis.
Although accumulating evidence suggests that unipolar depression, bipolar depression, and schizophrenia are marked by abnormalities in functional brain networks, 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 genetic contributors to the functioning of large-scale brain networks and characterize their relationship to dimensional symptom profiles in patients with affective and psychotic illnesses. This work translates cutting-edge neuroimaging, behavioral, and genomic approaches to the clinical arena, developing models that can predict individuals' clinical symptoms from their patterns of functional brain connectivity and elucidating the genetic and molecular mechanisms of network function in health and disease.
