Quantifying the relationship between the brain’s functional activity patterns and its structural backbone is crucial when relating the severity of brain pathology to disability in multiple sclerosis (MS). Network control theory (NCT) characterizes the brain’s energetic landscape using the structural connectome and patterns of brain activity over time. We applied NCT to investigate brain state dynamics and energy landscapes in controls and people with MS (pwMS). We also computed entropy of brain activity and investigated its association with the dynamic landscape’s transition energy and lesion volume. Brain states were identified by clustering regional brain activity vectors, and NCT was applied to compute the energy required to transition between these brain states. We found that entropy was negatively correlated with lesion volume and transition energy, and that larger transition energies were associated with pwMS with disability. This work supports the notion that shifts in the pattern of brain activity in pwMS without disability results in decreased transition energies compared to controls, but, as this shift evolves over the disease, transition energies increase beyond controls and disability occurs. Our results provide the first evidence in pwMS that larger lesion volumes result in greater transition energy between brain states and decreased entropy of brain activity.

Background: Neuronal 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 individual's 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 brain's functional activity and its structural backbone. One recently developed approach uses network control theory to characterize the brain's energetic landscape using an individual's 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. Aims: We 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 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 Methods: Ninety-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 individual's 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 model's feature importance assessed. Results: Six 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. Discussion: We 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.

The mapping between severity or location of this brain tissue damage and an individual's impairment remains challenging in multiple sclerosis (MS). Network control theory (NCT) can be used to characterize the brain's energetic landscape using an individual's structural connectome and brain activity dynamics. We apply brain activity dynamics analysis and NCT to functional time series and structural connectomes in controls and MS patients with and without disability and compare the three groups' resulting metrics. We also assessed the entropy of the three groups' brain activity time series. NCT was used to compute the energy required to transition between each pair of recurring dynamic brain states which were identified with k-means approach applied on fMRI time series. Larger transition energies were associated with MS patient who had disability compared to controls. Entropy was negatively correlated with lesion volume as was transition energy in MS. Functional compensation of brain activity in MS patients without disability may result in decreased transition energies between states compared to controls, but, as this mechanism diminishes, transition energies increase and disability occurs. Larger volumes of MS-related lesions may result in greater energetic demands to transition between brain states which then leads to lower overall entropy of brain activity.

Multiple Sclerosis (MS) is a disease that causes neuroinflammation and neurodegeneration in central nervous system. Neuroimaging techniques may enable us to better understand the neuropathological mechanisms of MS and how the brain may compensate the pathological changes. We identified the recurring dynamic brain states using fMRI data and investigated the energy required for the transition between those states using the network control theory approach. Our main findings are the dynamic states were visual, somatomotor, and frontoparietal states and the transition energy averaged over all transitions was significantly greater in the MS patients with disability compared to those without disability.