Inferring causality in highly-synchronized dynamics

The brain is a complex system with intricate neural dynamics, exhibiting interactions that are thought to be crucial for emergent cognitive functions. Causality methods provide a powerful tool for the characterization of these functional circuits by identifying directed functional interactions from time-series data. A frequently-stated hypothesis is that synchronization of oscillatory activity plays a key role for the communication of information between distant sites of the brain. However, quantitatively assessing the strength and the direction of these interactions has proven difficult, especially in the highly-synchronized states that are often observed. Here we explore how synchronization affects the capability to mathematically measure causal interactions in both artificial systems and data. Performing a comparative analysis of often-used causality metrics, we show how synchronization introduces biases. These results suggest a new framework that could be used to assess causality across a wide range of synchronization states in the brain and elsewhere.