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Cedalion Tutorial: A Python-based framework for comprehensive analysis of multimodal fNIRS & DOT from the lab to the

E Middell1,2, L Carlton3, S Moradi1,2

  • 1Intelligent Biomedical Sensing (IBS) Lab, Technische Universität Berlin, 10587 Berlin, Germany.

Arxiv
|January 16, 2026
PubMed
Summary

Cedalion is a new Python framework unifying functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) analysis. It enhances reproducibility and integrates machine learning for advanced neuroimaging workflows.

Keywords:
DOTdata driveneveryday neurosciencefNIRSmachine learningmultimodalphysiology

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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 for unified analysis of multimodal fNIRS and DOT data.
  • To provide a reproducible, extensible, and community-driven environment for advanced neuroimaging analysis.
  • To bridge established optical neuroimaging pipelines with ML frameworks for seamless multimodal data fusion.

Main Methods:

  • Cedalion integrates model-based and data-driven analyses, including forward modeling, optode co-registration, signal processing, GLM analysis, DOT image reconstruction, and ML methods.
  • The framework adheres to SNIRF and BIDS standards, supports cloud-executable Jupyter notebooks, and utilizes containerized workflows for scalable, reproducible analysis.
  • It incorporates validated algorithms for signal quality assessment, motion correction, GLM modeling, DOT reconstruction, and includes modules for simulation, data augmentation, and multimodal physiology analysis.

Main Results:

  • Cedalion provides a standardized architecture within the Python ecosystem, unifying diverse analytical methods.
  • The framework facilitates seamless integration with ML libraries (scikit-learn, PyTorch) and other neuroimaging modalities (EEG, MEG, physiological data).
  • A tutorial paper with seven executable notebooks demonstrates Cedalion's core features for laboratory-based and real-world neuroimaging.

Conclusions:

  • Cedalion offers an open, transparent, and community-extensible foundation for fNIRS and DOT data analysis.
  • It supports reproducible, scalable, cloud- and ML-ready neuroimaging workflows.
  • The framework empowers researchers by connecting established optical neuroimaging techniques with modern computational approaches.