pyAvalanches：用于分析神经元雪崩时空传播的Python软件包

中文摘要 AI 辅助

神经元雪崩的分析利用临界性框架为大脑动力学提供了见解，但研究的可重复性和可比性受到碎片化、实验室特定脚本的限制。为解决这一问题，我们推出了pyAvalanches，一个开源的Python软件包，提供从电生理记录（如脑电图-EEG）进行雪崩分析的标准化的端到端流程。从神经元雪崩的检测开始，该软件包提供其核心统计特征，包括大小和持续时间分布。除此之外，pyAvalanches的主要目标是表征雪崩期间活动传播的时空组织。为此，pyAvalanches的核心创新是计算雪崩转移矩阵（ATMs）以映射时空传播模式。在此基础上，该软件包从ATMs推导出基于网络的度量，将底层动态相互作用的拓扑和组织的研究与采用神经元雪崩框架的网络神经科学联系起来。整个工作流程封装在模块化且兼容scikit-learn的架构中。我们通过对一个公开的静息态EEG数据集进行示例性的组级分析，比较不同临床人群的传播模式，展示了pyAvalanches的实用性。通过提供用户友好、经过测试且可扩展的工具，pyAvalanches促进了可重复研究，支持开发基于雪崩的新型生物标志物，并使复杂的雪崩分析对更广泛的科学社区可及。该软件包有完整文档，并通过Python包索引（PyPI）分发。

英文摘要

The analysis of neuronal avalanches offers insights into brain dynamics utilizing the framework of criticality, but the reproducibility and comparability of studies are limited by the use of fragmented, lab-specific scripts. To address this issue, we introduce pyAvalanches, an open-source Python package providing a standardized, end-to-end pipeline for avalanche analysis from electrophysiological recordings (e.g., electroencephalography-EEG). Starting from the detection of neuronal avalanches the package provides their core statistical characterization, including size and duration distributions. Beyond this, the main aim of pyAvalanches is to characterize the spatiotemporal organization of activity propagation during avalanches. To this end, the core innovation of pyAvalanches is the compuation of Avalanche Transition Matrices (ATMs) to map spatiotemporal propagation patterns. Building on this, the package derives network-based metrics from the ATMs, bridging the study of the topology and organization of the underlying dynamical interactions with network neuroscience adopting the framework of neuronal avalanches. The entire workflow is encapsulated in a modular and scikit-learn compatible architecture. We demonstrate the utility of pyAvalanches through an illustrative group-level analysis on a public resting-state EEG dataset, comparing propagation patterns across different clinical populations. By providing a user-friendly, tested, and extensible tool, pyAvalanches facilitates reproducible research, enables the development of novel avalanche-based biomarkers, and makes complex avalanche analysis accessible to a broader scientific community. The package is fully documented and distributed via the Python Package Index (PyPI).

发表机构

• Sorbonne Université, Paris Brain Institute-ICM, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière（索邦大学，巴黎脑研究所-ICM，Inria，Inserm，AP-HP，皮蒂埃萨尔佩特里耶医院）
• University of Naples “Parthenope”（那不勒斯帕耳忒诺珀大学）
• Aix-Marseille University（艾克斯-马赛大学）

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