Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions

Abstract

Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. While large-scale spatial patterns of these networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. These temporal alterations were associated with white matter damage and were behavior-specific. Specifically, restoration of healthy brain dynamics paralleled recovery of cognitive functions, but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.Significance StatementUnderstanding the pathophysiology of a disorder is pivotal to design effective treatment. In this regard, recent advances in stroke research settled a new clinical concept: connectional diaschisis, which suggested that post-stroke impairments arise from both focal structural changes (tied to the injury) and widespread alterations in functional connectivity. fMRI time-resolved methods consider structural and temporal properties of brain networks as interdependent features. They are, thus, better suited to capture the intertwine between structural and functional changes. Here we leveraged a dynamic functional connectivity framework based on the clustering of hemodynamic-informed transients in a large and heterogeneous stroke population assessed longitudinally. We showed that lesions led to an unbalance in the brain dynamics that was associated with white matter fibers disruption and was restored as deficits recovered. Our work showed the potential of a time-resolved method to reveal clinically relevant dynamics of large-scale brain networks.