Event-Marked Windowed Communication: Inferring Activity Propagation from Neural Time Series

Tracking how activity or signal perturbations propagate in nervous systems is crucial to understanding interareal communication in the brain. Current analytical methodologies are not well suited to systematically infer interareal activity propagation from neural time series recordings. Here, we propose Event-marked Windowed Communication (EWC), a framework to infer activity propagation between neural elements by tracking the statistical consequence of spontaneous, endogenous regional perturbations. EWC tracks the downstream effect of these perturbations by subsampling the neural time series and quantifying statistical dependences using established functional connectivity measures. We test EWC on simulations of neural dynamics and demonstrate the retrieval of ground truth motifs of directional signaling, over a range of model configurations. We also show that EWC can capture activity propagation in a computationally efficient manner by benchmarking it against more advanced FC estimation methods such as transfer entropy. Lastly, we showcase the utility of EWC to infer whole-brain activity propagation maps from magnetoencephalography (MEG) recordings. Networks computed using EWC were compared to those inferred using transfer entropy and were found to be highly correlated (median r = 0.81 across subjects). Importantly, our framework is flexible and can be applied to activity time series captured by diverse functional neuroimaging modalities, opening new avenues for the study of neural communication.