Project Abstract Psychiatric disorders are among the most common illnesses across the lifespan, with more than 75% of individuals developing symptoms beginning in adolescence. Most psychiatric disorders include aspects of cognitive dysfunctions that have been suggested to predispose individuals to develop the psychiatric conditions and may serve as early markers of subsequent illness. Cognitive deficits and behavioral disturbances were indicated to be related to broad-based functional impairments across disorders, which traditional case-control studies are hard to capture. Following the Research Domain Criteria (RDoC) initiative launched by NIMH, there is an urgent need for developing biomarkers that can cross current diagnostic boundaries and drive new ways of defining psychiatric disorders based on dimensions of behavioral and neurobiological measures. In this project, we will quantify functional connectivity biomarkers that capture brain dysfunctions spanning multiple psychiatric disorders for an improved understanding of cognitive deficits and psychopathology. With high-density electroencephalography (EEG), we will quantify connectivity biomarkers predictive of individual cognitive behavior across the diagnostic spectrum (Aim 1). We will build a robust prediction model by combining relevance vector machine and connectome-based predictive modeling to identify transdiagnostic neural circuits that map the connectivity features to individual cognitive deficits. In Aim 2, we will design a dimensional approach based on multiway canonical correlation analysis to robustly reveal neural circuit-correlated dimensions of psychopathology. This approach allows us to jointly identify brain dysfunctions and dimensional behavioral phenotypes. We will evaluate these tools and compare the obtained results between EEG and fMRI using a large-scale transdiagnostic database from Healthy Brain Network. The proposed research will lead to an innovative and generalizable solution for the robust quantification of transdiagnostic EEG connectivity biomarkers that predict individual cognitive ability and delineate dimensions of psychopathological behavior across psychiatric disorders. Successful outcomes of the project will produce translatable biomarkers crossing current diagnostic boundaries in line with the goals of RDoC and provide a new avenue for EEG connectivity- based transdiagnostic study of psychopathology, thereby representing an important step towards the development of personalized therapeutics for improved mental health. We will release the developed tools to be publicly available to facilitate other transdiagnostic neuroimaging studies in psychiatry.