Characterization of 'Local' Functional Network Connectivity in 4D Spatial Dynamic fMRI Networks

The use of functional magnetic resonance imaging (fMRI) to map brain activity through functional network connectivity (FNC) has become a focal point in research. Most studies focus on static or dynamic FNC between predefined spatial network nodes, neglecting the possibility of time-varying dynamics within these spatial networks. While recent methods estimate voxel-level spatial dynamic networks, no approach has explored FNC between these spatial dynamic networks. In this study, we propose a novel method for examining FNC within spatially dynamic brain networks using human resting-state fMRI (rsfMRI) data. This method enables the calculation of network-specific FNC across (localized) voxel subsets. We applied this technique to the baseline dataset of 100 participants from the large-scale Adolescent Brain and Cognitive Development (ABCD) study. We first show our voxel-based FNC approach successfully replicates traditional static FNC results, demonstrating similar significant modularity in both the static FNC (sFNC) and global voxel FNC (GvFNC) matrices. The key advancement of our approach, however, lies in its ability to investigate local FNC within different voxel subsets. The findings reveal a reduction in anticorrelations within the average local voxel FNC (LvFNC) as the voxel inclusion rate decreases.