Automated MR Image Analysis in MS: Identification of a Surrogate

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Multiple sclerosis (MS) is a chronic central nervous system disease that affects 2.5 million patients worldwide. Currently, there is no cure for MS, but a number of disease modifying drugs have been either approved by the FDA or undergoing clinical trials. MS has a complex clinical course that includes unpredictable relapses and variable remissions. This makes clinical evaluation of MS difficult. The most commonly used clinical instruments for assessing the clinical status are limited in their sensitivity and can not detect subclinical activity. Thus, there is a need for identifying a surrogate that provides an objective and reproducible measure of the disease state. Magnetic resonance imaging (MRI) is the most sensitive imaging modality for noninvasively investigating MS. It is possible to derive a number of metrics that are based on multi-model MRI measurements that reflect different pathological aspects of MS. However, the correlation between the clinical status and various MRI-derived metrics is, at best, modest. This is, at least, in part due to the fact that many of the correlative studies are based on a single or a combination of a few MRI metric. A combination of MRI metrics that include gray matter, white matter, and spinal cord is expected to result in better correlation with clinical measures. The main objective of this proposal is to identify a surroagte that combines information from various MRI measures that include both brain and spinal cord. These studies will also identify and quantify the the so called "normal appearing tissue" in MS that is known to be pathological and thought to represent microscopic or diffuse pathology in MS. In order to realize the main bjective of this proposal, we wiil develop, implement, and evaluate a number of of advanced MRI acquisition and analysis, and image processing techniques. We will determine the longitudinal changes in the MRI-derived metrics in a cohort of MS patients and identify an optimum combination of these metrics that correlate with clinical disability as assessed by the extended disability status score (EDSS) and MS functional score (MSFC). The proposed multi-model MRI and longitudinal studies along with clinical evaluation should help identify appropriate surrogate(s), based on multiple MRI-derived metrics. Relevance to Public Health: Identification of surrogate in MS should revolutionize MS clinical trials, expedite technology transfer in neuropharmaceuticals and literally save millions of dollars in clinical trial expenses. The system should also empower clinicians in general to customize management of individual patients based on well-founded sound principles of the use of more widely available quantitative MRI. While the main emphasis is on MS, this system should be readily adaptable to investigate and manage various neurological disorders that require accurate determination of tissue volumes and their temporal change.