Title: Learning structural connectivity of the brain from imaging data
Authors: Jie Peng - University of California Davis (United States) [presenting]
Abstract: In recent years, there has been an explosion of multi-modal brain imaging data due to increasing interests in understanding functional and structural connectivity of the brain. One of the imaging technologies -- diffusion MRI (D-MRI) -- is an in vivo and non invasive technology that uses water diffusion as a proxy to probe architecture of biological tissues. D-MRI has been widely used in white matter fiber tracts reconstruction as well as many clinical applications including neurodegenerative diseases such as Alzheimer's disease. We will discuss how to extract structural connectivity information from D-MRI data. We will explore various models and challenges through both synthetic experiments and applications on data from large brain imaging consortium such as the Human Connectome Project (HCP) and Alzheimer's Disease Neuroimaging Initiative (ADNI).