Traditional conceptualization of mental disorders based on phenomenology is increasingly recognized as limited, but to date, we have lacked a clear path forward toward a more valid approach. Clinical heterogeneity and the imprecise nature of nosological distinctions represent fundamentally confounding factors limiting a better understanding of etiology, prevention and treatment. Neural connectivity of the major psychiatric disorders such as schizophrenia (SZ) and bipolar disorder (BP) has been variable across studies, which inarguably reflect multiple disease processes with distinct etiologies and overlapping clinical manifestations. Connectomics is an umbrella term that refers to scientific attempts to accurately map the set of neural elements and connections comprising the brain collectively referred to as the human connectome. Our application promises to uncover latent, homogenous, connectivity phenotypes using neuroimaging tools, which are free from the limitations of traditional diagnostic boundaries, and which correlate with clinical manifestations. SZ, BP and healthy control subjects will be scanned using the state-of-the-art """"""""Connectome Skyra"""""""", an optimized MRI scanner used by the NIH Human Connectome Project at Washington University, to obtain exceptionally high-resolution brain diffusion and functional connectivity images.
We aim to identify brain signatures and network patterns that relate to psychosis, affectivity and cognitive deficits across all groups using diffusion MRI and resting-state functional connectivity MRI. Our classification methods will employ computational tools that include graph theory and support vector machine based pattern classification to derive multiple segregate clusters of individuals with unique patterns of behavioral/cognitive profiles and brain connectivity. We will also use the novel unsupervised method of """"""""Non-Negative Matrix Factorization-Based Biclustering"""""""", which we developed for use on neuroimaging datasets to identify subgroups based on patterns of whole brain connectivity following voxelwise deconstruction of the entire brain's white matter tracts. Our application benefits from its multi-disciplinary collaborators and consultants, including several key investigators from the Human Connectome Project.

Public Health Relevance

Results from our studies would have a significant impact on those with mental disorders. Identifying coherent subpopulations within the spectrum of pathological changes would further our understanding of disease etiology, and identify subconditions that require alternative or modified interventions.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZMH1-ERB-L (05))
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Rumsey, Judith M
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Washington University
Schools of Medicine
Saint Louis
United States
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Godwin, Douglass; Ji, Andrew; Kandala, Sridhar et al. (2017) Functional Connectivity of Cognitive Brain Networks in Schizophrenia during a Working Memory Task. Front Psychiatry 8:294
Ji, Andrew; Godwin, Douglass; Rutlin, Jerrel et al. (2017) Tract-based analysis of white matter integrity in psychotic and nonpsychotic bipolar disorder. J Affect Disord 209:124-134
Hsieh, Christina J; Godwin, Douglass; Mamah, Daniel (2016) Utility of Washington Early Recognition Center Self-Report Screening Questionnaires in the Assessment of Patients with Schizophrenia and Bipolar Disorder. Front Psychiatry 7:149
Mamah, Daniel; Alpert, Kathryn I; Barch, Deanna M et al. (2016) Subcortical neuromorphometry in schizophrenia spectrum and bipolar disorders. Neuroimage Clin 11:276-286
Mamah, Daniel; Wen, Jie; Luo, Jie et al. (2015) Subcomponents of brain T2* relaxation in schizophrenia, bipolar disorder and siblings: A Gradient Echo Plural Contrast Imaging (GEPCI) study. Schizophr Res 169:36-45