ArticleNeurophotonics2026
Cedalion tutorial: a Python-based framework for comprehensive analysis of multimodal fNIRS and DOT from the lab to the everyday world.
Article in Neurophotonics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- SNIRF2BIDS: a GUI-based tool for converting functional near-infrared spectroscopy data to the Brain Imaging Data Structure in R.Neurophotonics · 2026Article
- Using large language models for enhancing accessibility for Monte Carlo photon transport simulations and beyond.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
- Update of
Authors and funding
13 authors.
Funding
Abstract
Functional near-infrared spectroscopy (fNIRS) and diffuse optical 1 tomography (DOT) are rapidly evolving toward wearable, multimodal, data-driven, and artificial-intelligence-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 modeling, photogrammetric optode coregistration, signal processing, general linear model (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 electroencephalography (EEG), magnetoencephalography (MEG), and physiological data. It implements validated algorithms for signal quality assessment, motion correction, GLM modeling, 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, and cloud- and ML-ready fNIRS/ DOT workflows for laboratory-based and real-world neuroimaging.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.