Transparent reporting is essential for inclusive and reproducible fNIRS research. Researchers should describe not only who was included, but also who was excluded, why exclusions occurred, how signal quality was evaluated, and how participant characteristics relevant to fNIRS measurement were handled.
This stage focuses on reporting practices that improve interpretability, reproducibility, and accountability.
What this stage covers
- Clear inclusion and exclusion criteria
- Recruitment methods and study context
- Hardware, probe configuration, placement framework, and preprocessing pipeline
- Participant demographics and relevant phenotypic characteristics
- Hair and skin characteristics relevant to optical signal quality
- Race and ethnicity reported separately from hair and skin characteristics
- Socioeconomic, language, education, and handedness information when relevant
- Channel-retention rates and data-quality thresholds
- Whether excluded participants differed from retained participants
- Open sharing of code, materials, protocols, and data where possible
- Funding sources and conflicts of interest
Current resources
- Webb et al. (2022), addressing racial and phenotypic bias in human neuroscience methods: https://www.nature.com/articles/s41593-022-01046-0
- Kwasa et al. (2023), demographic reporting and phenotypic exclusion in fNIRS: https://doi.org/10.3389/fnins.2023.1086208
- Yücel et al. (2025), fNIRS signal quality, hair and skin characteristics, and suggested metadata table: https://doi.org/10.1038/s41562-025-02274-7
- Rogers (2025), doctoral dissertation on inclusive fNIRS: https://www.proquest.com/openview/ee1fad2d6f11fe0be6eb0dddb26a08a0/1?pq-origsite=gscholar&cbl=18750&diss=y
Suggested additions
We welcome reporting checklists, example metadata tables, data-quality reporting templates, examples of transparent exclusion reporting, sample language for reporting signal-quality limitations, and examples of reporting participant characteristics relevant to fNIRS measurement.
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