Hidden Markov Modeling Reveals Altered Functional Network Dynamics in Multiple Sclerosis
Purpose: Multiple sclerosis (MS) is a neurodegenerative and demyelinating disease of the central nervous system. Recent evidence suggests that localized gray matter atrophy in MS may be involved in distributed functional brain networks. However, the dynamic trajectory of functional network alterations in MS remains unclear. The current study extends prior network-mediated findings and aims to investigate the dynamic functional connectivity (dFC) and energetic landscape across an atrophy-prone network in MS. Methods: Twenty MS participants and twenty healthy controls (HC) underwent resting-state fMRI. The atrophy-based functional network (AFN) model was implemented, and time series from atrophy-prone regions were fit to a 12-state hidden Markov model. Each functional network state, a recurring pattern of activity across the AFN, was characterized by the AFN regions with the strongest activation. The states were then grouped into two metastates. Dynamic functional network metrics were compared between groups by permutation testing and correlated with disability and cognition. The energetic landscape of the network was mapped with a pairwise maximum-entropy model. Temporal irreversibility was also measured, which defines the arrow-of-time asymmetry of network dynamics. Results: People with MS showed lower occupancy of state 1, which peaked on the right middle frontal and anterior cingulate cortex. Occupancy and mean lifetime were higher in states 3 and 10 (p < 0.05). In state 3, cortical activity was below the network average, and state 10 peaked on the postcentral gyrus. At the metastate level, MS participants spent more time in metastate M1 and less in M2 (p = 0.04). Within the MS group, a longer interval between visits to state 3 was associated with greater disability (r = 0.57), and a higher switching rate in state 10 with poorer verbal fluency (r = -0.50). The energetic landscape was similar between groups. Temporal irreversibility was lower in MS, mainly across subcortical connections. Conclusion: MS participants dwelled longer in specific dFC states of the AFN, while the rate of transition between states did not differ from healthy controls. Dynamic functional network metrics were associated with disability and cognitive performance and may serve as functional correlates of clinical impairment. The states were similar between groups, whereas the directed drive between states was lower across subcortical connections. This pattern suggests altered network dynamics rather than altered network architecture in MS. Findings from this study characterize the functional network dynamics of neurodegeneration in MS, which may inform future development of network-based approaches for monitoring disease progression.