Statistical Methods for Neuroimaging

3 credits · Graduate School

This course covers standard and modern/advanced methods for neuroimage analysis from a biostatistical perspective. Students will learn to analyze and interpret common modalities such as fMRI, structural MRI, cortical thickness, diffusion-weighted imaging, and resting-state connectivity using popular neuroimaging analysis software and visualization tools. Advanced topics may include, site correction, first-level and group-level models, network analysis, AI/machine learning, circularity analysis, multivariate/spatial inference, confidence set methods, and centile methods. Upon completion of this course, students will be prepared to understand and contribute to statistical research in neuroimaging. This course is open to biostatistics PhD students only; for students in other programs, instructor permission is required to enroll. Prerequisites: BIOS 6312 and BIOS 6342. [3].