Abstract #4871

Diagnosis of Multiple Sclerosis Subtype through Machine Learning Analysis of Frontal Cortex Metabolite Profiles

Abhinav V. Kurada¹, Kelley M. Swanberg^1,2, Hetty Prinsen², and Christoph Juchem^1,2,3,4

¹Biomedical Engineering, Columbia University School of Engineering and Applied Science, New York, NY, United States, ²Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States, ³Neurology, Yale University School of Medicine, New Haven, CT, United States, ⁴Radiology, Columbia University Medical Center, New York, NY, United States

The onset and progression of multiple sclerosis (MS) is accompanied by changes in brain biochemistry. Magnetic resonance spectroscopy (MRS) is a powerful tool for investigating these changes in vivo. Machine learning analysis of MRS-derived biochemical profiles may reveal metabolic patterns inherent in certain MS subtypes to inform their diagnosis. By employing a feature set of only metabolite concentrations derived from brain MRS data acquired at 7 Tesla, we achieved an 80% validation set accuracy for differentiating MS patients from healthy controls and a 70% validation set accuracy for differentiating relapsing-remitting and progressive MS patients.

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