Multidimensional brain connectome features of depression and anxiety

PROJECT SUMMARY Disrupted communication between brain regions responsible for emotion regulation (limbic and higher cognitive cortical regions) may critically underlie the emotional dysregulation that characterizes mood and anxiety disorders. However, the most instructive metric of such communication has yet to be agreed upon. In addition, due to technical barriers, there are currently no fine-grained measurements of limbic subregion connectivity in humans. A network- based approach can be used to explore limbic and cortical subregion connectivity (i.e. connectome), shedding light on the discrete or shared neural mechanisms underlying core symptoms in mood and anxiety disorders. In the proposed study we will (1) characterize small limbic subregions and whole-brain connectomes in healthy controls and individuals with major depressive and generalized anxiety disorder, by optimizing and employing a novel 7-Tesla MRI protocol with improved spatial resolution with whole brain coverage; (2) discern network based biomarkers of depression and anxiety by developing and characterizing a multi-modal integrative brain network consisting of structural, functional and dynamic topographies using a new computational multilayer approach; and (3) transdiagnostically examine how connectome organization manifests across the three study groups. By portraying specific limbic subregion involvement in the brain connectome, this would advance our understanding of how connectome alternations manifest in psychiatric disorders. Furthermore, this work combines high-resolution measures of brain activity and synchronization (functional MRI), with maps of the brain’s white matter architecture and anatomical connections (diffusion MRI). Their combined study will facilitate identification of aberrant network features and their contributions to core symptoms in depression and anxiety. Lastly, the transdiagnostic approach will uncover shared and unique network mechanisms of depression and anxiety that could potentially provide improved diagnostics and treatment selection. This Career Development Award will allow for the critical complimentary training goals centering on: (1) gaining practical clinical experience towards identifying clinical needs and conducting translational research; (2) development of technical advanced MRI sequence development skills; (3) refine computational skills in advanced network science methods. The proposed research and training afforded by this award will allow me to launch an independent research program developing neuroimaging methods to study brain network perturbations in mood and anxiety disorders.