The Time-Varying Network Patterns in Motor Imagery Revealed by Adaptive Directed Transfer Function Analysis for fMRI

Abstract

Motor imagery (MI) is a multi-dimensional high-level cognitive ability that involves coordinated contributions from multiple brain regions [i.e., supplemental motor area (SMA), premotor cortex (M1), and posterior parietal cortex] and their couplings as well. However, the dynamic interactions among these activated regions during MI are still unclear. Here, we applied the adaptive directed transfer function (ADTF) to track time-varying connectivity patterns among activated regions during MI. We found that the connectivity patterns are different in two MI tasks and dynamically changes over time, representing special state-dependent and timing-dependent time-varying connectivity patterns. Our findings indicate that left anterior insula (aIns), contralateral SMA, and contralateral M1, which served as important causal targets, play crucial roles in reorganization of the network at different stages, implying that there exists a hierarchical network reorganization during MI. In addition, we found that the lateralization of the left- and right-hand MI occurred in the MI middle stages and was reflected by the effective connectivities modulated by contralateral SMA. Moreover, a graph analysis was adopted to further characterize the temporal evolution of these interactions among activated regions. We also found that network efficiencies are coordinated with the connectivity pattern of networks during MI. Collectively, these findings based on the ADTF analysis expand our understanding of the time-varying network organization in MI.