Amit Akula Rose Hills
DT-MRI Visualization of the brains optical networks to understand MS Pathology
While researchers have not been able to fully characterize the pathology of multiple sclerosis (MS), conventional Magnetic Resonance Imaging (MRI) techniques have been the gold standard for the diagnosis and monitoring of MS (Guglielmetti, Lassmann). Using advanced MRI techniques, such as diffusion weighted imaging, and specifically spherical deconvolution tractography to image the brains neural tracts, recent studies have furthered MRIs predictive capabilities to leverage the brains connectivity to develop a composite MRI-based measure of motor network integrity that appears to predict disability substantially better than conventional non-network based MRI measures (Pardini). My research project would be to conduct similar analysis of the brains visual network: the white matter tracts underlying the parts of the brain associated with visual processing. Using data from the Human Connectome Project, I intend to develop a brain atlas of the visual pathways. I will then use this atlas to predict visual function in MS patients. By correlating my analysis of the visual network with two standard measures of visual strength, visual evoke potentials (VEP) and optic coherence tomography (OCT), I hope to use these visual pathways to better understand MS pathology (Martinez-Lapiscina).