Pattern Classification Using Magnetic Resonance Imaging in Traumatic Brain Injury

New York University School Of Medicine, New York NY

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Abstract

PROJECT SUMMARY Mild traumatic brain injury (MTBI) affects ~1.5 million persons annually in the United States with fifteen to 30% of patients suffering long-term disability after injury. We remain in the early phase of understanding this disease and one of the greatest barriers to studying the disease and developing appropriate therapy is the difficulty in diagnosis and outcome prediction. Generally, the diagnosis of MTBI relies on using the Glasgow Coma Scale (GCS), a 15-point gross measurement of eye-opening, motor and verbal response. The National Institute for Neurological Disorders and Stroke (NINDS) workshop in 2014 indicated that use of GCS score as a single classifier for TBI is insufficient and proposed that neuroimaging play a larger role towards the development of objective criteria for diagnosis and outcome prediction. We have specific experience in studying novel MRI techniques that show much promise in evaluating MTBI patients. The goal of the current proposal is to bring these novel MRI techniques to clinical use. We propose to combine information from objective MR imaging features with clinical information to learn the patterns that can best distinguish patients from controls and predict long-term outcome using machine learning. We will validate our tool using a separate subject cohort. Such a tool would be an extremely powerful clinical tool to identify at-risk patients for early intervention. Additionally, this research will identify the most clinically relevant MR metrics, thereby pointing the way to novel therapeutic pathways.