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 hypothesized that this MS-specific model would successfully predict performance on the N-back task in the validation cohort (n _validation = 36). In addition, we assessed the generalizability of existing WM networks derived in healthy young adults to these samples, and we explored anatomical differences between the healthy and MS networks.

Results:

We successfully derived an MS-specific predictive model of WM in the internal sample (full: r _s = 0.47, permuted p = 0.011), but the predictions were not significant in the validation cohort (r _s = −0.047; p = 0.78, mean squared error [MSE] = 0.006, R ² = −2.21%). In contrast, the healthy networks successfully predicted WM in both MS samples (internal: r _s = 0.33 p = 0.049, MSE = 0.009, R ² = 13.4%; validation cohort: r _s = 0.46, p = 0.005, MSE = 0.005, R ² = 16.9%), demonstrating their translational potential.

Discussion:

Functional networks identified in a large sample of healthy individuals predicted significant variance in WM in MS. Networks derived in small samples of people with MS may have limited generalizability, potentially due to disease-related heterogeneity. The robustness of models derived in large clinical samples warrants further investigation. ClinicalTrials.gov ID: NCT03244696.

Impact statement

Working memory deficits in people with multiple sclerosis have important consequences for employment, leisure, and activities of daily living. Identifying a functional connectivity-based marker that accurately captures individual differences in working memory may offer a useful target for cognitive rehabilitation. We demonstrate that machine learning can be applied to whole-brain functional connectivity data to identify networks that predict individual-level working memory in people with multiple sclerosis. However, existing network-based models of working memory derived in healthy adults outperform those identified in multiple sclerosis, suggesting translational potential of brain networks derived in large, healthy samples for predicting cognition in multiple sclerosis.