Events Calendar

Imaging-driven Translational Research: Engineering Solutions for Applications in Neurology

Speaker: Mehul P. Sampat, Ph.D., Department of Neurology, Yale School of Medicine

Talk Title: Imaging-driven Translational Research: Engineering Solutions for Applications in Neurology

Abstract: The World Health Organization estimates that neurological diseases affect millions of people around the world. Many neurological diseases are difficult to detect in the early stages when molecular and physiological changes are generally subtle. With disease progression, the treatment and care of patients with advanced neurological conditions puts a greater burden on the patient, their families and society.

Magnetic resonance imaging (MRI) has evolved as a core tool for the diagnosis, longitudinal monitoring and scientific investigation of a number of neurological disorders. MRI provides a noninvasive assessment of brain structures and a more objective and quantitative measure as compared to clinical scores. However, given the complexity of neurological disorders and the huge volume of imaging data, analyzing this data is very challenging. My research focuses on the development of new engineering solutions to address challenging clinically motivated research questions. Specifically, I aim to: (a) Discover imaging based biomarkers of important disease mechanisms implicated in neurological disorders (b) Apply these biomarkers for longitudinal monitoring and prediction of disease progression and (c) Develop new quantitative imaging-based metrics of drug response and treatment efficacy.

In this talk, I will describe my past, current and future work towards these goals. First, I will describe a project, in which I have shown that regional MRI-based variables can be used to classify patients into the different subtypes of Multiple Sclerosis. A key aspect of this work is that the results from a cross-sectional dataset were tested in an independent longitudinal data set. Then I will describe my investigations on the effects of Multiple Sclerosis on cortical gray matter. The major aims of this work were to compare cortical thickness of patients against healthy controls and to estimate the predictive value of cortical thickness on physical impairment over time. This will be followed by a discussion on the reliability of MRI-derived measurements. I will summarize the results from a new model for assessing and quantifying the sources of variability in MRI derived measurements. A key feature of this work is its flexibility and adaptability, which makes it applicable to study the effect of various factors on any imaging-based quantitative measurement. Finally, I will conclude my talk by presenting my future research directions in (a) extracting biomarkers from Diffusion Tensor Imaging and Magnetic Resonance Spectroscopy and (b) developing quantitative imaging based metrics for the evaluation of treatment response and efficacy.

Biography: Dr. Mehul Sampat received his Ph.D. from the University of Texas at Austin in 2006. Following this, he was a postdoctoral research fellow at the Center for Neurological Imaging at Brigham and Women’s Hospital and Harvard Medical School. From 2009 to 2010, he was a staff researcher in the Department of Neurology at UCSF. Since January 2011, he has been appointed as a junior faculty member in the Dept. of Neurology at Yale School of Medicine. His research interests include neuro-informatics and biomedical image analysis & pattern recognition. He has published 15 peer-reviewed journal articles and more than 30 conference papers and abstracts. Since 2005, his work has been cited 350 times.

Dr. Sampat received a pre-doctoral fellowship from the Congressionally Directed Medical Research Program from 2004 to 2006. In 2011, he was awarded approximately 1 million computing hours from the NSF sponsored TeraGrid initiative for performing computational neuro-imaging research.

Host: Prof. Richard Leahy

Location: Hughes Aircraft Electrical Engineering Center (EEB) -

Audiences: Everyone Is Invited

Contact: Talyia Veal