Schizophrenia

Development of Individual Differences in Adolescent Brain Structure and Risk 2024 Award: $150,989 Executive function is a critical ability that leads to better life outcomes, while low levels of executive function are a risk factor for mental illness. Therefore, characterizing neurocognitive trajectories of executive function and how they relate to future mental illness is a public health imperative. The main goal of this proposal is to identify aspects of neurocognitive trajectories from birth that predict the emergence of symptoms of mental illness in emerging adulthood. This research will move us closer to developing strategies for early identification and intervention by characterizing when and how neurocognitive trajectories related to executive function diverge to indicate risk for future mental illness. Need/Problem: High levels of executive function are associated with greater educational attainment, job success, and overall quality of life, while low [...]

White Matter Connectome and Behavior Relationships in Early Childhood 2023 Award: $27,044 Despite increasing research on the association between brain structure and cognition/behavior in adults, their relationship in early childhood remains largely unknown. We propose a new method to study how the brain’s networks relate to behavior in early childhood: connectome-based predictive modeling. Our prediction model will enable the early identification of children at high risk of developing psychiatric disorders. Need/Problem: There is strong evidence that white matter connectome computed from diffusion MRI is associated with cognition or behavior in adults and older children. However, little is known about their relationship in early childhood. Grant Summary: We will investigate how the brain’s networks relate to behavior. We will use the white matter connectome, which shows how different brain area connect to each other. We will develop a novel deep [...]

Developing and Evaluating a Computable Phenotype for Treatment-Resistant Schizophrenia 2021 Award: $41,302 Treatment-Resistant Schizophrenia affects about 30% of Schizophrenia patients. Reliable identification of TRS patients within an Electronic Health Record (EHR) system will improve patient care and enhance clinical research. We will develop a computable phenotyping algorithm by combining several information technologies to characterize TRS patients from an EHR system. Need/Problem: Treatment-Resistant Schizophrenia (TRS) affects about 30% of schizophrenia patients. However, the utilization rate of clozapine, the only approved antipsychotic for TRS, remains low. Characterization of TRS patients from Electronic Health Records will facilitate early detection of TRS patients and subsequently increase the use of clozapine. Grant Summary: We will use an array of information technologies (database query, temporal medication mining, and natural language processing) to develop an algorithm that could quickly characterize TRS patients in an Electronic Health [...]