Structure-function multilayer network integration and cognition in multiple sclerosis

People with multiple sclerosis (MS) often present with cognitive deficits that cannot fully be attributed to focal brain alterations. Whole-brain network changes show stronger relations, but MS network insights have mostly focused on either structural or functional (single-layer) networks, while recent work has shown the importance of multilayer frontoparietal network integration for cognition. Here, we explored the cognitive relevance of multilayer integration of the frontoparietal network in relapsing-remitting MS (n=780) using diffusion and resting-state functional MRI. Cognitive relations were first assessed for nodal multilayer eigenvector centrality, averaged over frontoparietal network nodes as a measure of integration; and post-hoc for mean eccentricity for both single-layers and the multilayer. Higher multilayer frontoparietal network centrality was associated with worse SDMT performance ({beta} = -0.117, p = 0.005). Mean eccentricity of single-layer diffusion ({beta} = -0.123, p < 0.001) and multilayer networks ({beta} = 0.085, p = 0.018) were associated with cognition. However, results could not be replicated using a different anatomical parcellation. This study showed that cognition in MS is related to multilayer network parameters. Nevertheless, correlations were weak and atlas-specific, suggesting that a binary structure-function multilayer network approach is not particularly relevant as a correlate of cognition in MS.