Predicting cognition using estimated structural and functional connectivity networks and artificial intelligence in multiple sclerosis

Our prior work demonstrated that estimated structural and functional connectomes (eSC and eFC) generated using multiple sclerosis (MS) lesion masks and artificial intelligence (AI) models can predict disability as effectively as SC and FC derived from diffusion and functional MRI in MS. The goal of this study was to assess the ability of eSC and eFC in predicting baseline and 4-year follow-up cognition in MS patients. The Network Modification tool was performed to estimate SC from the clinical MRI-derived lesion masks. The eSC was then used as an input to Krakencoder, an encoder-decoder model, to estimate FC. The highest accuracy was obtained when predicting the follow-up Symbol Digit Modalities Test (SDMT) using regional eSC or eFC with a median Spearman's correlation of 0.58 for eSC and 0.56 for eFC, which is higher or similar to other studies that predicted cognition in healthy and diseased cohorts. Decreased eSC and eFC in the cerebellum and increased eFC in the default mode network were associated with lower follow-up SDMT scores. Our findings demonstrate that eSC and eFC derived from clinically acquired MRI and AI models can effectively predict cognition. The use of lesion-based estimates of connectome disruptions may potentially improve cognition-related individualized treatment planning.