TranCIT: Transient Causal Interaction Toolbox
It provides a user-friendly, validated solution for neuroscientists investigating transient causal dynamics in complex systems, though it is incremental as it implements existing methods in a new package.
The paper tackles the challenge of quantifying transient causal interactions from non-stationary neural signals by introducing TranCIT, an open-source Python toolbox that successfully captures causality in high-synchrony regimes and identifies known transient information flow in real-world data.
Quantifying transient causal interactions from non-stationary neural signals is a fundamental challenge in neuroscience. Traditional methods are often inadequate for brief neural events, and advanced, event-specific techniques have lacked accessible implementations within the Python ecosystem. Here, we introduce trancit (Transient Causal Interaction Toolbox), an open-source Python package designed to bridge this gap. TranCIT implements a comprehensive analysis pipeline, including Granger Causality, Transfer Entropy, and the more robust Structural Causal Model-based Dynamic Causal Strength (DCS) and relative Dynamic Causal Strength (rDCS) for accurately detecting event-driven causal effects. We demonstrate TranCIT's utility by successfully capturing causality in high-synchrony regimes where traditional methods fail and by identifying the known transient information flow from hippocampal CA3 to CA1 during sharp-wave ripple events in real-world data. The package offers a user-friendly, validated solution for investigating the transient causal dynamics that govern complex systems.