Schizophrenia

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 [...]

Probing Gene-environment Interactions in Schizophrenia and Stress: Integrating human GWAS and mouse models 2020 Award: $60,000 Schizophrenia is a heritable disorder that is impacted by environmental influences like stressful life experience. Studies have demonstrated thousands of genetic differences are associated with schizophrenia; however we do not understand the impact of these differences, or how stress experience may influence these differences in DNA. In this project we use molecular biology techniques to examine identified differences in the DNA of people suffering from schizophrenia in stress models. This research will help us to identify mechanisms that lead to schizophrenia vulnerability. Need/Problem: Schizophrenia is a heritable disorder, but it is influenced by environmental factors. One of these factors is stressful life experience. Due to ethical considerations, it is impossible to test how stress experience influences the expression of genes in neurons of persons suffering from schizophrenia.  Grant Summary: Dysregulation of [...]

Dissection of Top-down Cortical Circuits Underlying Auditory Oddball Response, a Biomarker for Schizophrenia 2020 Award: $40,000 Hallucination, or “hearing voices” is one of the most prevalent symptoms of schizophrenia. In this project we will study how internally generated signals in the brains affect sensory processing.  Findings in the simple mouse brains provide a first step toward the understanding of circuit mechanisms underlying broad perceptual symptoms in schizophrenic patients. Need/Problem: Schizophrenia is a severe mental disorder that affects 1 in 100 individuals. Schizophrenic patients suffer from symptoms such as hallucinations (hearing voices), delusions, and disordered thinking. Grant Summary: We will study how hallucination, or hearing internally generated sounds, occurs by investigating information processing in the neural circuits within the auditory cortex Goals and Projected Outcomes: Insights into the interplay between sensory inputs and internally generated signals within the brain will [...]