Early Neighborhood Contexts Shapes Neuropsychiatric Risk in Children 2026 Award: $58,540 It is widely known that development is shaped by a complex interplay of biological and environmental factors. However, the variables most often used to operationalize environmental effects are unable to capture the broad neighborhood- and community-level contexts that shape development. Our research aims to determine which elements of the environment have the strongest impact on cognition, risk for psychiatric illness, and brain structure over time, paving the way for more targeted interventions aimed at mitigating risks associated with poorer environmental exposures. Need/Problem: Environmental factors interact with brain development to influence children's development across domains. To date, however, the examination of such environmental characteristics has largely been limited to familylevel indicators, with little consideration of key neighborhood and community contexts that can shape development across time. Grant Summary: We [...]

Biomarkers of Persistent Cancer-Related Cognitive Impairment in Breast Cancer Survivors 2026 Award: $57,240 Many cancer survivors experience lasting memory and concentration problems after treatment, often alongside depression, anxiety, or stress. This project will study biological signals in breast cancer survivors to better understand how cognitive and mental health symptoms may be connected and help guide the development of future mechanism-based treatments that improve long-term brain health for cancer survivors. Need/Problem:At least 1 in 3 cancer survivors experience persistent problems with memory, attention, and thinking after cancer treatment, often called “chemobrain.” These cognitive symptoms frequently occur alongside depression, anxiety, and stress and can interfere with work, daily functioning, and quality of life. Despite how common these challenges are, we have no established treatments. Grant Summary:This project will examine biological markers in the blood of long-term breast cancer survivors to better [...]

A Machine Learning Approach to Classify Medical Records by Psychiatric Diagnosis 2023 Award: $35,784 The widespread adoption of electronic health records (EHRs) has been accompanied by the enticing promise of using “big data” to improve patient care and clinical research. Many “big data” models rely on discrete data points, while in the field of mental health, our outcomes and diagnostic criteria are typically found in unstructured clinical narratives. In this study, we will train and evaluate machine learning classification models of psychiatric diagnoses using software capable of mining structured and unstructured data in the EHR, by incorporating unstructured clinical data using natural language processing. Need/Problem: The widespread adoption of electronic health records (EHRs) is accompanied by the enticing promise of using “big data” to improve patient care through large-scale research studies, population health and quality improvement initiatives, and improved [...]