Objectives/Goals: This project, at the intersection of psychological and neuroimaging science, aims to develop and validate an open, transparent, and reproducible workflow for measuring functional brain activity using functional near-infrared spectroscopy (fNIRS), advancing methodological rigor and translational potential in human neuroscience. Methods/Study Population: A Python-based workflow using MNE was designed to improve reproducibility in fNIRS data acquisition. The workflow includes the following: (1) electromagnetic digitizer accuracy testing and distortion correction; (2) integration of head shape and source-detector location data; (3) visualization tools for verifying optode placement accuracy before data collection; and (4) implementation entirely in an open-source software to promote accessibility and transparency. Results/Anticipated Results: Difference in data quality, spatial accuracy, and reproducibility between standard fNIRS practice and this open workflow will be systematically evaluated. Discussion/Significance of Impact: This project advances translation in neuroimaging by embedding rigor and transparency into all stages of the workflow. By openly sharing software tools and validation methods, the workflow enables independent replication, fosters methodological transparency, and reduces reliance on proprietary systems.
Calvert et al. (Wed,) studied this question.