Tutorial unites optical brain imaging tools in a single Python framework
Functional near-infrared spectroscopy (fNIRS) measures brain activity optically based on changes in cortical blood flow. Unlike magnetic resonance imaging, the technique is wearable, so it can be used during movement and outside the lab. Diffuse optical tomography (DOT) extends this principle with improved spatial and depth information. Both are often combined with other modalities, such as…
A new Python framework called Cedalion unifies various optical brain imaging techniques, including functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT). These methods, which measure brain activity through optical changes in blood flow, are often combined with other modalities like electroencephalography (EEG) and physiological signals.
Cedalion brings all these tools together in a single, user-friendly environment, lowering the barriers to research and analysis. The framework integrates simulations of light propagation, photogrammetric estimation of optode positions, signal quality assessment, statistical modeling, image reconstruction, and machine learning methods.
By adhering to open data standards like SNIRF and BIDS, Cedalion enables researchers to share complete, retraceable analyses. The tutorial, written for neuroscientists, engineers, and data scientists, consists of seven hands-on Jupyter notebooks that can be run directly in the cloud. Cedalion's development is community-driven and open-source, built on the established tools Homer2/3 and AtlasViewer.
While the framework aims to lower the effort of sharing analyses and promote data-driven approaches, it faces limitations such as high computational costs and the need for systematic benchmarking.
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