USING A METHOD BASED ON MACHINE LEARNING ON AN ENORMOUS MULTIFOCAL MAGNETIC RESONANCE IMAGING DATA SET, FORECASTING THE INFORMATION COMPUTATIONAL EFFICIENCY FOR MULTIPLE SCLEROSIS

The Symbol Digit Modalities Test (SDMT) has been suggested as a reliable screening tool for information processing speed (IPS) deficiencies, which are common in MS patients. Understanding the mechanisms underlying cognitive problems in multiple sclerosis has significantly improved because to magnetic resonance imaging (MRI). It is yet unknown, nevertheless, which structural MRI signals have the most correlation with cognitive function. We extracted multimodal data (demographic, clinical, neuropsychological, and structural MRIs) from 540 MS patients using the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative