Speaker: Selena Wang March 12, 2025 3pm Luddy AI 2005 Zoom: https://iu.zoom.us/j/89307757306
Title: Neuroimaging connectivity analysis needs network science for brain-behavior linking
Abstract: The brain is comprised of interacting neurons, and its complexity poses significant challenges for researchers to understand its structure, function, and dynamics. Statistical network analysis (SNA), a collaboration between network science and statistics has emerged as a powerful tool to understand the generative process of such interconnected systems and integrate multimodal brain imaging data. In this talk, I present two methodology innovations under SNA to improve the current landscape for imaging biomarker detection. With the latent variables-assisted statistical network analysis (LatentSNA), we substantially improve the statistical power for identifying biomarkers; and as a result, we discover large, star-like brain functional architectures implicated in the development of internalizing symptoms in 5,000 to 7,000 children of the Adolescent Brain Cognitive Development (ABCD) study. This finding supports internalizing as a complex and involving psychological phenomenon whose development involves large-scale affective interference of multiple coordinating functional systems. The proposed methods have broad applicability and can contribute to many domains of science with rigorous and powerful analysis.
Bio: Dr. Wang is an Assistant Professor in the Department of Biostatistics and Health Data Science, Indiana University School of Medicine. Her primary research interest is to develop scalable statistical and computational approaches for high-dimensional biomedical data with a focus on network science, high dimensional data analysis, neuroimaging, data integration and statistical machine learning. She has extensive experiences developing MRI analytic methods, Bayesian inference and latent space network models incorporating biological, psychological, and medical information. She also has experiences in translational research, providing support on study design and analyses for studies investigating psychiatric outcomes, mental disorders, aging, Alzheimer’s disease, etc.