Title: Multimodal brain-connectivity biomarkers for profiling heterogeneity in early psychosis Psychotic disorders involve dysfunction in complex structural and functional brain connectivity. But the current clinical approach for diagnosing psychotic disorders using the Diagnostic and Statistical Manual of Mental Illness (DSM) usually fails to categorize the diseases based on biological abnormalities. Identifying the specific abnormal brain system of the individual patient, especially for patients at the early psychosis (EP) stage before irreversible brain alterations take place, is key to develop more effective early intervention approaches. For this purpose, we propose to develop an innovative data-driven approach to characterize the heterogeneity of brain abnormalities in early psychosis patients across different clinical diagnostic categories. We will develop and apply our approach to two datasets of subjects from the ?Human Connectome Project for Early Psychosis? where high-quality magnetic resonance imaging (MRI) data and clinical measures were collected from 320 patients and 80 controls and the CIDAR project with 46 patients and 37 controls. To characterize psychosis- related brain connectivity, we propose a novel approach to integrate our diffusion MRI measures on microscopic structures, such as axon density, and our resting-state functional MRI measure on the information flow through the axonal bundles. Then we will apply a systematically designed set of steps, including selecting brain connectivity features, canonical correlation analysis, and cross-validation, to define several novel EP- networks based on multimodal brain connectivity markers. Our approach will provide novel brain-network profiles to understand patient-specific abnormalities. Results from this project could provide important brain targets for developing more effective personalized treatment approaches.
This project aims to define data-driven characterization of the heterogeneity of brain abnormalities in early psychosis using multimodal imaging measures on brain structural and functional connectivity. We will analyze data sets in the Human Connectome Project for Early Psychosis and the CIDAR project to define novel brain- network components related to early psychosis based on fingerprints in brain abnormalities characterized by joint analysis of diffusion MRI and resting-state functional MRI measures. Results from this project will provide novel brain-network based profiles to understand brain abnormities in early psychosis, which could provide important brain targets for developing more effective personalized treatment approaches.
| Ning, Lipeng; Rathi, Yogesh (2018) A Dynamic Regression Approach for Frequency-Domain Partial Coherence and Causality Analysis of Functional Brain Networks. IEEE Trans Med Imaging 37:1957-1969 |
