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Cedalion tutorial: a Python-based framework for comprehensive analysis of multimodal fNIRS and DOT from the lab to
Eike Middell1,2, Laura B Carlton3, Shakiba Moradi1,2
1Technische Universität Berlin, Intelligent Biomedical Sensing (IBS) Lab, Berlin, Germany.
Neurophotonics
|August 14, 2026
Summary
Cedalion is a new Python framework unifying functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) analysis. It enables reproducible, scalable, and AI-ready neuroimaging workflows, integrating various analytical tools for enhanced research.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Biomedical Engineering
Background:
- Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are advancing towards wearable, multimodal, and AI-supported neuroimaging.
- Current analytical tools for fNIRS and DOT are fragmented, hindering reproducibility, interoperability, and integration with machine learning (ML) workflows.
Purpose of the Study:
- To introduce Cedalion, a Python-based open-source framework designed to unify advanced model-based and data-driven analysis of multimodal fNIRS and DOT data.
- To provide a reproducible, extensible, and community-driven environment for fNIRS and DOT data analysis.
- To facilitate the integration of fNIRS/DOT data with ML workflows and other neuroimaging modalities.
Main Methods:
- Cedalion integrates forward modeling, optode coregistration, signal processing, GLM analysis, DOT image reconstruction, and ML-based methods within a standardized Python architecture.
- The framework adheres to SNIRF and BIDS standards, supports cloud-executable Jupyter notebooks, and utilizes containerized workflows for scalable, reproducible analysis.
- It connects optical neuroimaging pipelines with ML frameworks (scikit-learn, PyTorch) and supports multimodal fusion with EEG, MEG, and physiological data.
Main Results:
- Cedalion provides validated algorithms for signal quality assessment, motion correction, GLM modeling, and DOT reconstruction.
- Includes modules for simulation, data augmentation, and multimodal physiology analysis.
- Demonstrates core features through seven fully executable tutorial notebooks.
Conclusions:
- Cedalion offers an open, transparent, and community-extensible foundation for fNIRS and DOT neuroimaging.
- It supports reproducible, scalable, cloud- and ML-ready analysis workflows for both laboratory and real-world settings.
- The framework enhances the integration of advanced analytical techniques and multimodal data fusion in optical neuroimaging research.
