The arrow-of-time in neuroimaging time series identifies causal triggers of brain function

Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping physical phenomena. However, almost all current time series metrics do not exploit this asymmetry, probably due to the difficulty to account for it in modeling frameworks. Here, we introduce an AoT-sensitive metric that captures the intensity of causal effects in multivariate time series, and apply it to high-resolution functional neuroimaging data. We find that causal effects underlying brain function are more distinctively localized in space and time than functional activity or connectivity, thereby allowing us to trace neural pathways recruited in different conditions. Overall, we provide a mapping of the causal brain that challenges the association paradigm of brain function.

Automated summary

Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. An arrow-of-time (AoT)-sensitive metric is introduced to capture the intensity of causal effects in multivariate time series of high-resolution functional neuroimaging data. Causal effects underlying brain function are found to be more distinctly localized in space and time than functional activity or connectivity, allowing for tracing neural pathways recruited in different conditions. Overall, a mapping of the causal brain is provided, challenging the association paradigm of brain function.