Employing Connectome-Based Models to Predict Working Memory in Multiple Sclerosis.

ABSTRACT: Introduction: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory (WM), but the search for neural correlates of WM within circumscribed areas has been inconclusive. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual differences in WM. Materials and Methods: We applied connectome-based predictive modeling to functional magnetic resonance imaging data from WM tasks in two independent samples with relapsing-remitting MS. In the internal sample (n_internal = 36), cross-validation was used to train a model to predict accuracy on the Paced Visual Serial Addition Test from functional connectivity. We hypothesiz

SUBMITTER: Manglani HR 

PROVIDER: S-EPMC10039278 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature