fMRI brown bag: March 27, 2026

Yu-Wei Wang is a postdoctoral associate at Yale School of Medicine in the Yale Imaging and Psychopharmacology Lab (PI: Sarah Yip). Her current research leverages large-scale fMRI and connectomics to build reproducible, generalizable brain–behavior biomarkers, with a focus on adolescent sleep, cognition, and psychopathology. She has contributed to methodological work on multisite harmonization and the application of transfer learning to precision psychiatry, including publications in Biological Psychiatry, NeuroImage and Imaging Neuroscience.

Time: 12:00-1:00pm

Location: Moore Hall, Consortium for Interacting Minds

From Big to Small: Emerging Methods for Enhancing Precision Psychiatry Through Transfer Learning

Abstract: Identifying reliable links between individual differences in neurobiological features and differences in symptom profiles and treatment outcomes is a primary goal of precision psychiatry. In this context, brain-behavior predictive modeling has emerged as a powerful approach for elucidating the neural mechanisms underlying both basic cognitive functions and complex clinical phenomena. However, the widespread adoption of these methods in clinical settings is often hindered by the limited amount of neuroimaging and clinical data available for specific patient populations. Transfer learning—a widely adopted strategy in machine learning and deep learning that extracts generalizable and transferable associations from complex, high-dimensional datasets—offers a promising solution. By leveraging large-scale neuroimaging datasets from consortia, transfer learning enables the fine-tuning of models to generate accurate predictions in smaller, clinically specific datasets. I will provide a conceptual and practical overview of transfer learning approaches applied to brain-behavior modeling, with a focus on their utility in predicting clinical outcomes. I will also discuss recent studies demonstrating that models pretrained on large population datasets can be adapted to reliably predict clinical features from previously unseen neuroimaging data, thereby enhancing model generalizability and interpretability. Finally, I will address practical and theoretical considerations for the adoption of these methods, underscoring their potential to advance mechanistic understanding and bolster clinical utility in precision psychiatry.