Abstract
ABSTRACTUnderstanding the brain changes underlying cognitive dysfunction is a key priority in multiple sclerosis to improve monitoring and treatment of this debilitating symptom. Functional connectivity network changes are associated with cognitive dysfunction, but it is less well understood how changes in normal appearing white matter relate to cognitive symptoms. If white matter tracts share a similar network structure it would be expected that tracts within a network are similarly affected by MS pathology. In the present study, we used a tractometry approach to explore patterns of variance in diffusion metrics across white matter (WM) tracts. We investigated whether separate networks, based on normal variation or pathology, appear, and how this relates to neuropsychological test performance across cognitive domains. A sample of 102 relapsing-remitting MS patients and 27 healthy controls underwent MRI and neuropsychological testing. Tractography was performed on diffusion MRI data to extract 40 WM tracts and microstructural measures were extracted from each tract. Principal component analysis (PCA) was used to decompose metrics from all tracts to assess the presence of any co-variance structure among the tracts. Similarly, PCA was applied to cognitive test scores to identify the main cognitive domains. Finally, we assessed the ability of tract components to predict test performance across cognitive domains. We found that a single component which captured pathology across all tracts explained the most variance and that there was little evidence for separate, smaller network patterns of pathology. WM tract components were weak, but significant, predictors of cognitive function in MS. These findings highlight the need to investigate the relationship between the normal appearing white matter and cognitive impairment further and on a more granular level, to improve the understanding of the network structure of the brain in MS.
Publisher
Cold Spring Harbor Laboratory