Time-resolved brain network community detection based on instantaneous phase of fMRI data

In this paper, we propose a novel method that estimates time-resolved communities, or networks, from parcellated fMRI data based on instantaneous phase of the parcel timeseries. Importantly, each community label reference a limited phase range across time and subjects. It thus avoids the relabelling problem that is common for community detection algorithms. Our aim was to enhance the temporal resolution of brain network analysis in a whole-brain context. The method provides insights into how brain regions synchronize both within and across subjects and reorganize during task performance. It offers a complementary perspective on network integration and segregation as concurrent processes, quantified through differences in instantaneous phase. We employed HCP motor task fMRI data to exemplify its practical application. Prior to calculation of instantaneous phase, signal timeseries were decomposed into two signal modes with minimally overlapping frequency ranges. We show that task specific motor movements (hand, feet, tongue) can be separated from block-design related activation (visual and attention networks) where the former was found in the slower mode and the latter in the higher frequency mode.