Predicting Neurostimulation Responsiveness with Dynamic Brain Network Measures

Transcranial direct current stimulation (tDCS) shows great promise in enhancing neurocognitive abilities. However, the neurostimulation responsiveness varied hugely. Our previous work demonstrates that people receiving tDCS stimulation over Temporoparietal Junction (TPJ) fall into two heterogeneous groups: the positive responders who benefit and the negative responders who hurt from tDCS. The present study investigated whether dynamic brain network properties of resting-state fMRI could predict the pattern. We calculated each subsystem of the default mode network’s dynamic attributes using the multilayer community detection algorithm. Results indicated that the recruitment indexes were significantly different in bilateral aMPFC, PCC, Rsp, and PHC regions between positive responders and negative responders. Our results also confirm the advantages of the dynamic network measures over the static network measures. The study provides a feasible protocol in establishing the pre-stimulation screening procedure using resting-state fMRI.