My research as director of the Neuroinformatics Development Lab at the Buffalo Neuroimaging Analysis Center focuses on developing and applying quantitative image analysis methods to neuroimaging data in order to characterize better the onset, progression, and treatment of neurological diseases. In particular, magnetic resonance imaging (MRI) can provide a vast amount of raw data about a variety of brain and spinal cord tissue characteristics, but extracting meaningful clinical and research metrics from these data is still challenging. Modern computer science techniques, however, can play a transformative role in helping physicians assess data they receive from neuroimaging techniques in order to deliver the best possible care to their patients.
Highlights of my work include translational approaches to measuring brain atrophy in clinical routine MRI, methods for quantification of longitudinal myelin changes in vivo, more precise algorithms for tracking gray and white matter changes over time, and connectomics research elucidating the functional and structural networks involved in cognitive changes in multiple sclerosis. This work has had a substantial impact on our understanding of multiple sclerosis (MS) onset and progression, and these techniques have also been successfully applied in clinical trials to understand better the impact of various therapeutic approaches in MS.
My ongoing research in quantitative image analysis is aimed at increasing our understanding of the data available from state-of-the-art neuroimaging. This increased understanding can be directly translated to clinicians to better inform their patient diagnoses and treatment decisions.