# TranCIT: Transient Causal Interaction Toolbox

## TL;DR

TranCIT is an open-source Python toolbox that enables accurate detection of transient causal interactions in non-stationary neural signals, addressing limitations of traditional methods especially during brief neural events.

## Contribution

It introduces a comprehensive analysis pipeline with novel causal measures, filling a gap in accessible tools for transient causality detection in neuroscience.

## Key findings

- Successfully captures causality in high-synchrony regimes
- Identifies hippocampal CA3 to CA1 causal flow during ripples
- Provides a validated, user-friendly Python package

## Abstract

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.