Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system. Structures affected in MS include the corpus callosum, connecting the hemispheres. Studies have shown that in mammalian brains, structural connectivity is organized according to a conservation principle, an inverse relationship between intra- and interhemispheric connectivity. The aim of this study was to replicate this conservation principle in subjects with MS and to explore how the disease interacts with it. A multicentric dataset has been analyzed including 513 people with MS and 208 healthy controls from seven different centers. Structural connectivity was quantified through various connectivity measures, and graph analysis was used to study the behavior of intra- and interhemispheric connectivity. The association between the intra- and the interhemispheric connectivity showed a similar strength for healthy controls ( r = 0.38, p < 0.001) and people with MS ( r = 0.35, p < 0.001). Intrahemispheric connectivity was associated with white matter fraction ( r = 0.48, p < 0.0001), lesion volume ( r = −0.44, p < 0.0001), and the Symbol Digit Modalities Test ( r = 0.25, p < 0.0001). Results show that this conservation principle seems to hold for people with MS. These findings support the hypothesis that interhemispheric connectivity decreases at higher cognitive decline and disability levels, while intrahemispheric connectivity increases to maintain the balance. Author Summary In our study, we investigated how multiple sclerosis (MS), a disease that affects the central nervous system, impacts brain connectivity and the conservation principle of connectivity in the brain across hemispheres. By analyzing data from 513 MS patients and 208 healthy individuals, we examined if this conservation principle holds and how it changes due to MS. Our findings revealed that both MS patients and healthy individuals exhibit a similar balance between connections within each hemisphere and between hemispheres. We also observed that as cognitive impairment and disability in MS patients increase, interhemispheric connectivity decreases while intrahemispheric connectivity compensates. This suggests that the brain attempts to maintain balance despite the disease’s progression, highlighting the adaptability of neural connectivity during the course of MS.

In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or gray matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural, and functional brain connectivity networks. The aim of this study was to combine the morphological, structural, and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analyzing multiple types of relational data from the same objects simultaneously using graph- mining techniques. The main contribution of this research is the design, development, and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with gray matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified. Author Summary This study presents the design, development, and validation of a framework that merges morphological, structural, and functional brain connectivity networks into one multilayer network. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that some brain regions with a synchronized connectivity deterioration could be identified.