fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience

Yücel, MA ORCID: 0000-0002-4291-2847, Luke, R ORCID: 0000-0002-4930-8351, Mesquita, RC ORCID: 0000-0003-1945-6713, von Lühmann, A ORCID: 0000-0002-4995-293X, Mehler, DMA, Lührs, M, Gemignani, J, Abdalmalak, A, Albrecht, F, de Almeida Ivo, I, Artemenko, C ORCID: 0000-0001-5947-7960, Ashton, K, Augustynowicz, P, Bajracharya, A ORCID: 0000-0002-7361-6020, Bannier, E ORCID: 0000-0002-8942-7486, Barth, B, Bayet, L ORCID: 0000-0002-0323-7949, Behrendt, J, Khani, HB ORCID: 0000-0001-5495-3964, Borot, L et al (2025) fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience. Communications Biology, 8 (1). ISSN 2399-3642

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Abstract

As data analysis pipelines grow more complex in brain imaging research, understanding how methodological choices affect results is essential for ensuring reproducibility and transparency. This is especially relevant for functional Near-Infrared Spectroscopy (fNIRS), a rapidly growing technique for assessing brain function in naturalistic settings and across the lifespan, yet one that still lacks standardized analysis approaches. In the fNIRS Reproducibility Study Hub (FRESH) initiative, we asked 38 research teams worldwide to independently analyze the same two fNIRS datasets. Despite using different pipelines, nearly 80% of teams agreed on group-level results, particularly when hypotheses were strongly supported by literature. Teams with higher self-reported analysis confidence, which correlated with years of fNIRS experience, showed greater agreement. At the individual level, agreement was lower but improved with better data quality. The main sources of variability were related to how poor-quality data were handled, how responses were modeled, and how statistical analyses were conducted. These findings suggest that while flexible analytical tools are valuable, clearer methodological and reporting standards could greatly enhance reproducibility. By identifying key drivers of variability, this study highlights current challenges and offers direction for improving transparency and reliability in fNIRS research.

Item Type:	Article
Uncontrolled Keywords:	Brain; Humans; Spectroscopy, Near-Infrared; Reproducibility of Results; Research Personnel; Data Accuracy; Spectroscopy, Near-Infrared; Reproducibility of Results; Humans; Data Accuracy; Brain; Research Personnel; 31 Biological Sciences; 32 Biomedical and Clinical Sciences; Neurosciences; Generic health relevance; Spectroscopy, Near-Infrared; Reproducibility of Results; Humans; Data Accuracy; Brain; Research Personnel; 31 Biological sciences; 32 Biomedical and clinical sciences
Subjects:	R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions:	Sport and Exercise Sciences
Publisher:	Nature Research
Date of acceptance:	18 June 2025
Date of first compliant Open Access:	29 August 2025
Date Deposited:	29 Aug 2025 09:33
Last Modified:	29 Aug 2025 09:45
DOI or ID number:	10.1038/s42003-025-08412-1
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27017

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