Schizophrenia

Investigation of D2 and 5-HT2A Genetic Variants on Antipsychotic Responsiveness 2025 Award: $57,705 Many patients with schizophrenia endure months or even years of suboptimal treatment before finding an antipsychotic that works. This project aims to change that by identifying how individual genetic differences in brain receptors affect treatment response. The ultimate goal: a personalized tool that helps doctors choose the right antipsychotic the first time. Need/Problem: Antipsychotics are the cornerstone of schizophrenia treatment, yet patient responses vary widely. Many patients endure a lengthy trial-and-error process during medication selection, which often leads to prolonged symptoms and hospitalization, unnecessary side effects, and poor long-term outcomes. A precision-medicine approach is urgently needed to guide antipsychotic selection using a person’s unique genetic information. Grant Summary: This study will analyze how genetic variations in two key neuroreceptors—D2 and 5-HT2A—affect a patient’s long-term clinical stability [...]

Modeling genetic risk for schizophrenia in human-derived neurons 2025 Award: $60,000 Over 75% of the risk for developing schizophrenia is determined by the genes one inherits. In this project, the Mealer and Heinzen Labs plan to leverage recent advances in genetic studies of schizophrenia and neurons generated from human stem cells to better understand changes in brain development and function that could one day be used design better treatments for severe mental illness. Need/Problem: Antipsychotic medications, which are used to treat many different psychiatric conditions including schizophrenia, were not developed based on our knowledge of what is changed in the brain or how these disorders are inherited. As such, these treatments commonly provide limited symptom relief and do not appear to alter disease course or offer a chance at cure. Thus, there is incredible opportunity and need for rationally [...]

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 [...]