𝓗1 persistent features of the resting-state connectome in healthy subjects

Abstract

Abstract The analysis of the resting-state functional connectome commonly relies on graph representations. However, the graph-based approach is restricted to pairwise interactions, not suitable to capture high-order interactions, that is, more than two regions. This work investigates the existence of cycles of synchronization emerging at the individual level in the resting-state fMRI dynamic. These cycles or loops correspond to more than three regions interacting in pairs surrounding a closed space in the resting dynamic. We devised a strategy for characterizing these loops on the fMRI resting state using persistent homology, a data analysis strategy based on topology aimed to characterize high-order connectivity features robustly. This approach describes the loops exhibited at the individual level on a population of 198 healthy controls. Results suggest that these synchronization cycles emerge robustly across different connectivity scales. In addition, these high-order features seem to be supported by a particular anatomical substrate. These topological loops constitute evidence of resting-state high-order arrangements of interaction hidden on classical pairwise models. These cycles may have implications for the synchronization mechanisms commonly described in the resting state.