开源工具TranCIT可精准捕捉神经信号中的瞬时因果关系。
TranCIT: Transient Causal Interaction Toolbox
- 基于格兰杰因果、转移熵等方法，融合动态因果强度分析
- 在高同步状态下仍能检测到传统方法失效的因果效应
- 适合研究脑区间瞬时信息流，尤其适用于尖波涟漪事件
从非平稳神经信号中量化瞬时因果交互是神经科学中的基础挑战。传统方法对短暂神经事件检测能力不足，而现有先进事件特异性技术又缺乏可靠的Python实现。本文提出TranCIT（Transient Causal Interaction Toolbox），一个开源Python工具包，涵盖格兰杰因果、转移熵及更鲁棒的基于结构因果模型的动态因果强度（DCS）与相对动态因果强度（rDCS），构建完整分析流程。我们证明其在高同步状态下成功捕捉传统方法失效的因果关系，并在真实数据中识别出海马CA3到CA1在尖波涟漪事件中的已知瞬时信息流。该工具提供用户友好的验证方案，适用于复杂系统中瞬时因果动态的研究。
原文摘要 · Abstract (English)
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.
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