Cedalion

Cedalion is a new Python-based toolbox designed for advanced multimodal fNIRS analysis and Diffuse Optical Tomography (DOT) that can be collaboratively expanded by the community. The toolbox is developed by our lab in close collaboration with the BOAS Lab at the BU Neurophotonics Center. Cedalion is currently work in progress and will be launched in time for the SfNIRS Conference in Birmingham, UK, in September 2024.

Cedalion aims to integrate core features of HOMER3 and AtlasViewer for fNIRS data analysis and image reconstruction while tapping into the rich Python ecosystem of modern tools for data analysis, machine learning, and multimodal integration. It will provide user-extensible data structures that allow for easy exchange with frameworks such as PyTorch, SciKit-Learn and Pandas to simplify the integration of conventional fNIRS data processing streams and machine learning workflows. The toolbox relies on the BIDS and SNIRF standards to support the integration and data exchange with other toolboxes designed for the preprocessing and analysis of physiological recordings, behavioral measures and other neuroimaging modalities, further facilitating multimodal analysis. To read the full abstract that introduces cedalion on the SfNIRS 2024 conference, click here.

Cedalion is growing on a weekly basis and will continue to do so for the next months and years. Our goal for it is to provide functionality in the following domains:

– S-o-A- fNIRS preprocessing (mBLL conversion, quality assessment, artifact rejection, General Linear Model – GLM)
– advanced methods for diffuse optical tomography (DOT), including image reconstruction and processing in image/voxel space
– optode coregistration using photogrammetry
– ML/AI methods for single-trial analysis, blind-source-separation, and advanced analysis
– multimodal analysis of fNIRS/DOT with systemic physiology and other auxiliary signals

Cedalion is community driven and it is important to us to design it from ground up to credit contributors and help them and their methods gain visibility once they incorporated into the framework. Come back soon to learn more, and please contact us if you are interested in early exchange on how to best spearhead your own (sub)packages for Cedalion.