Accurate clinical course descriptions (phenotypes) of multiple sclerosis (MS) are important for communication, prognostication, design and recruitment of clinical trials, and treatment decision-making. Standardized descriptions published in 1996 based on a survey of international MS experts provided purely clinical phenotypes based on data and consensus at that time, but imaging and biological correlates were lacking. Increased understanding of MS and its pathology, coupled with general concern that the original descriptors may not adequately reflect more recently identified clinical aspects of the disease, prompted a re-examination of MS disease phenotypes by the International Advisory Committee on Clinical Trials of MS. While imaging and biological markers that might provide objective criteria for separating clinical phenotypes are lacking, we propose refined descriptors that include consideration of disease activity (based on clinical relapse rate and imaging findings) and disease progression. Strategies for future research to better define phenotypes are also outlined.

Multiple sclerosis affects more than 2 million people worldwide and is currently incurable. A number of interventions to modify the course of multiple sclerosis have been developed that offer new insight into disease mechanisms.

Immunometabolism as therapeutic target: Dimethyl fumarate (DMF) is an immunomodulatory compound used to treat multiple sclerosis and psoriasis whose mechanisms of action remain only partially understood. Kornberg et al. found that DMF and its metabolite, monomethyl fumarate, succinate the glycolytic enzyme GAPDH (see the Perspective by Matsushita and Pearce). After DMF treatment, GAPDH was inactivated, and aerobic glycolysis was down-regulated in both myeloid and lymphoid cells. This resulted in down-modulated immune responses because inflammatory immune-cell subsets require aerobic glycolysis. Thus, metabolism can serve as a viable therapeutic target in autoimmune disease. Science , this issue p. 449 ; see also p. 377

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.