Event-marked Windowed Communication: Inferring activity propagation from neural time series

Tracking signal propagation in nervous systems is crucial to our understanding of brain function and information processing. Current methods for inferring neural communication track patterns of sustained co-activation over time, making them unsuitable to detect discrete instances of signal transmission. Here, we propose Event-marked Windowed Communication (EWC), a new analytical framework to infer functional interactions arising from discrete signalling events between neural elements, in otherwise continuous time series data. In contrast to conventional measures of functional connectivity, our method utilises an event-based subsampling of neural time series, which allows it to capture the statistical analogue of activity propagation. We test EWC on simulations of neural dynamics and show that it is capable of retrieving ground truth motifs of directional signalling, over a range of model configurations. Critically, we demonstrate that EWC's subsampling approach affords profound reductions in computation times, compared to established network inference methods such as transfer entropy. Lastly, we showcase the utility of EWC to infer whole-brain functional networks from MEG recordings. Networks computed using EWC and transfer entropy were highly correlated (median r=0.821 across subjects), but EWC inference was approximately 6.5 times faster per epoch. In summary, our work presents a new method to infer signalling from time series of neural activity at low computational costs. Our framework is flexible and can be applied to activity time series captured by diverse functional neuroimaging modalities, opening up new avenues for the study of neural communication.