You are leaving this site You are about to leave www.grantformultiplesclerosisinnovation.org/. The content of the site you are about to visit is not controlled by www.grantformultiplesclerosisinnovation.org/.

Darin T Okuda

GMSI 2017_Darin Okuda

Darin T Okuda, MD, MS, FAAN, FANA

Dr Okuda is a clinician-scientist and professor specializing in multiple sclerosis within the Department of Neurology and Neurotherapeutics at UT Southwestern Medical Center in Dallas, Texas. Dr Okuda completed his undergraduate, graduate and medical education at the University of Hawaii. He received his residency training in neurology at the Barrow Neurological Institute and went on to complete a fellowship in neuroimmunology at the University of California, San Francisco Multiple Sclerosis Center. Within UT Southwestern, he currently serves as Director of the Neuroinnovation Program, Director of the Multiple Sclerosis and Neuroimmunology Imaging Program, and Deputy Director of the MS Program at the Clinical Center for Multiple Sclerosis.

Dr. Okuda’s current research focuses on innovative approaches involving the evaluation, diagnosis and management of patients with multiple sclerosis. He is both nationally and internationally recognized for his work in defining and investigating radiologically isolated syndrome (RIS) and currently directs scientific strategies within the Radiologically Isolated Syndrome Consortium (RISC), a multi-national working group aimed at advancing the science of the very early forms of CNS demyelination. In addition to this work, his background involves translational research specific to novel efforts aimed at creating the next generation of diagnostic metrics and unique platforms for disease surveillance. He has designed and created highly successful technological programs, devices and mobile applications that are being utilized for education, research and patient care within the neuroscience field.

Dr Okuda is a diplomate of The American Board of Psychiatry and Neurology, Inc., fellow of the American Academy of Neurology, fellow of the American Neurological Association, and member of the American Academy of Neurology Committees on Neuro-imaging and Ethics.


The diagnosis of multiple sclerosis (MS) requires fulfillment of both clinical and radiological criteria. Included are key radiological tenets comprising a requisite number of lesions having a specific character (i.e. size, shape and morphology) and spatial distribution pattern with involvement of periventricular, juxtacortical, infratentorial and spinal cord regions. The effective application of existing dissemination in space criteria is hindered by the heterogeneity of lesions resulting from various etiologies, concomitant radiological features stemming from age-related changes identified in result of the highly sensitive nature of magnetic resonance imaging (MRI) technologies, and the lack of additional radiological characteristics beyond two-dimensional descriptions. The proposed investigation aims to evaluate 3-dimensional (3D) changes in the spatial pattern and surface characteristics of lesions from clinically well-phenotyped patients with MS or non-specific white matter disease (NSWM) longitudinally while generating machine-learning algorithms to recognize distinct geometric shape, surface, and MRI intensity pattern differences between the two groups that are indiscernible to the human eye. These will be validated by comparing the acquired 3D MRI data and associated characteristics to histopathological data from post-mortem specimens. These data may complement existing MRI approaches designed to improve lesion specificity to better enhance diagnosis, recognize concomitant NSWM disease in patients with MS and vascular risk factors, provide a rapid computerized platform capable of discerning the origin of lesions beyond spot visualization and the use of time consuming post-processing techniques, and ultimately provide insights into the biology of these disease states. Overall, these data may result in a paradigm shift in the way MS is diagnosed and evaluated.

Content provided by the researcher.


Back to Winners page