统一多模态fNIRS/DOT分析，让脑成像研究更可复现、易扩展。
Cedalion Tutorial: A Python-based framework for comprehensive analysis of multimodal fNIRS & DOT from the lab to the everyday world
- 基于Python构建一体化分析框架，整合建模与数据驱动方法
- 支持云端运行、ML融合与标准化数据格式，提升可复现性
- 适合脑科学、神经工程及多模态数据研究者使用
功能近红外光谱（fNIRS）和漫射光学断层成像（DOT）正快速向可穿戴、多模态及数据驱动的日常环境神经成像发展。然而，现有分析工具分散于不同平台，限制了可复现性、互操作性以及与现代机器学习（ML）流程的集成。Cedalion 是一个基于 Python 的开源框架，旨在统一多模态 fNIRS 与 DOT 数据的模型驱动与数据驱动分析，在可复现、可扩展且社区共建的环境中实现全流程整合。该框架集成了前向建模、摄影测量光学探头配准、信号处理、GLM 分析、DOT 图像重建以及基于 ML 的数据驱动方法，采用标准化的 Python 生态架构。其遵循 SNIRF 与 BIDS 标准，支持可执行的 Jupyter 笔记本云端运行，并提供容器化工作流，实现可扩展、完全可复现的分析管道，可随原始论文一同发布。Cedalion 将传统光学神经成像流程与 scikit-learn、PyTorch 等 ML 框架无缝衔接，支持与 EEG、MEG 及生理数据的多模态融合。框架包含经验证的信号质量评估、运动校正、GLM 建模与 DOT 重建算法，还配备仿真、数据增强与多模态生理分析模块。自动化文档链接每个方法至其原始发表文献，持续集成测试保障稳健性。本教程论文提供七个可完整运行的笔记本，演示核心功能。Cedalion 提供开放、透明、社区可扩展的基础，支持实验室与真实世界神经成像的可复现、可扩展、云原生与机器学习就绪的工作流。
原文摘要 · Abstract (English)
Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are rapidly evolving toward wearable, multimodal, and data-driven, AI-supported neuroimaging in the everyday world. However, current analytical tools are fragmented across platforms, limiting reproducibility, interoperability, and integration with modern machine learning (ML) workflows. Cedalion is a Python-based open-source framework designed to unify advanced model-based and data-driven analysis of multimodal fNIRS and DOT data within a reproducible, extensible, and community-driven environment. Cedalion integrates forward modelling, photogrammetric optode co-registration, signal processing, GLM Analysis, DOT image reconstruction, and ML-based data-driven methods within a single standardized architecture based on the Python ecosystem. It adheres to SNIRF and BIDS standards, supports cloud-executable Jupyter notebooks, and provides containerized workflows for scalable, fully reproducible analysis pipelines that can be provided alongside original research publications. Cedalion connects established optical-neuroimaging pipelines with ML frameworks such as scikit-learn and PyTorch, enabling seamless multimodal fusion with EEG, MEG, and physiological data. It implements validated algorithms for signal-quality assessment, motion correction, GLM modelling, and DOT reconstruction, complemented by modules for simulation, data augmentation, and multimodal physiology analysis. Automated documentation links each method to its source publication, and continuous-integration testing ensures robustness. This tutorial paper provides seven fully executable notebooks that demonstrate core features. Cedalion offers an open, transparent, and community extensible foundation that supports reproducible, scalable, cloud- and ML-ready fNIRS/DOT workflows for laboratory-based and real-world neuroimaging.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。