Abstract
BACKGROUND: Cognitive impairment is common in multiple sclerosis (MS), yet the application of diagnostic frameworks of Neurocognitive Disorders (NCDs) is limited. Additionally, the integration of multimodal data for predicting cognitive outcomes using artificial intelligence (AI) remains underexplored. This study aimed to characterize NCDs in MS and predict cognitive worsening using an explainable deep learning model trained on MRI and clinical data. METHODS: Two-hundred twenty-four MS patients and 115 healthy controls (HC) underwent 3.0 T MRI and clinical assessment at baseline. MS patients also completed neuropsychological testing, including estimation of z-cognitive reserve, at baseline and after a median follow-up of 3.4 (interquartile range = [2.0; 6.1]) years. MS patients were classified as Mild or Major NCD according to the Diagnostic and Statistical Manual of Mental Disorders criteria at baseline, and as "stable" or "worsened" based on cognitive changes at follow-up. A deep learning model was trained on baseline T1-weighted MRI, demographic, clinical, and brain volumetric data to predict cognitive decline, with explainability methods used to interpret the model's decisions. RESULTS: At baseline, 4% of patients had Mild and 11% Major NCD. At follow-up, 12% showed cognitive decline. The deep learning model predicted follow-up cognitive status with 90% accuracy. Explainability models identified the most relevant predictors, in order of importance: cortical gray matter volume, age, thalamic and hippocampal volumes, T2 lesion volume, and z-cognitive reserve. CONCLUSIONS: The proposed multimodal AI approach demonstrated robust performance and highlighted relevant brain regions associated with cognitive worsening, underscoring its potential for personalized cognitive assessment and monitoring in MS.