Congratulations to authors postdoctoral research scientist Dr. Kelley Swanberg and laboratory alumni Mr. Abhinav Kurada and Dr. Hetty Prinsen on their publication entitled "Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles." This work represents a novel application of supervised classification algorithms, including support vector machines, K-nearest neighbors, and quadratic discriminant analysis, demonstrating their capacity to distinguish between multiple sclerosis phenotypes on the basis of untransformed 7-Tesla 1H-MRS-visible normal-appearing prefrontal cortex tissue metabolite concentrations alone, while showing that distinct metabolites were differentially important for identifying multiple sclerosis status and phenotype. Further details can be found in the paper here.
