Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity

Abstract

BackgroundNeuronal loss, demyelination, and inflammation in the brains of people with multiple sclerosis (pwMS) can cause impairment or disability across a range of domains. However, the mapping between severity or location of this brain tissue damage and an individuals impairment remains challenging. Advanced neuroimaging techniques may enable us to better understand the neuropathological or compensatory mechanisms in MS and quantify the link between the brains functional activity and its structural backbone. One recently developed approach uses network control theory to characterize the brains energetic landscape using an individuals structural connectome and brain activity dynamics. While brain activity dynamics have previously been analyzed in MS cohorts, investigating how structural network damage, one of the hallmarks of MS, relates to shifts in activity dynamics has not yet been performed. AimsWe apply brain activity dynamics analysis and network control theory (NCT) to functional time series and structural connectomes in healthy controls (HC) and pwMS with and without disability and compare the three groups resulting metrics. We also assessed the entropy of the three groups brain activity time series. Our hypotheses were that pwMS with disability would have increased brain state transition energies compared to pwMS without disability and controls, and that greater energies would be related to greater lesion burden and decreased entropy of brain activity. Materials and MethodsNinety-nine pwMS and 19 HC were included in our study; individuals underwent MRI, including anatomical, diffusion and resting-state functional scans. Expanded Disability Status Scale (EDSS) was used to assess disability; 66 pwMS with EDSS<2 were considered as not having disability. Commonly recurring brain states were identified using k-means clustering of the regional brain activity time series. NCT was used to compute the energy required to transition between each pair of states or to remain in the same state, taking into account an individuals structural connectome. The entropy of brain activity time series was quantified and correlated with overall transition energy and lesion volume. Finally, logistic regression with ridge regularization was used to classify the groups using transition energies and demographics/clinical information and the models feature importance assessed. ResultsSix brain states were identified, including pairs of states with high and low visual (VIS+/-), ventral attention (VAN)+/- and somatomotor (SOM+/-) network activity. Global entropy was negatively correlated with lesion volume (r=-0.20, p=0.03) as was global transition energy (r=-0.18, p=0.04) in pwMS. Smaller transition energies were associated with pwMS without disability, while larger transition energies were associated with pwMS with disability. DiscussionWe conjecture that functional compensation of brain activity in pwMS without disability results in decreased transition energies between states compared to controls, but, as this mechanism diminishes, transition energies increase and disability occurs. We also conjecture that larger volumes of MS-related lesions result in greater energetic demands to transition between brain states which then leads to lower overall entropy of brain activity.