Yücel, Meryem A., Luke, Robert, Mesquita, Rickson C., von Lühmann, Alexander, Mehler, David M. A., Lührs, Michael, Gemignani, Jessica, Abdalmalak, Androu, Albrecht, Franziska, de Almeida Ivo, Iara, Artemenko, Christina, Ashton, Kira, Augustynowicz, Paweł, Bajracharya, Aahana, Bannier, Elise, Barth, Beatrix, Bayet, Laurie, Behrendt, Jacqueline, Khani, Hadi Borj, Borot, Lenaic, Borrell, Jordan A., Brigadoi, Sabrina, Brink, Kolby, Bulgarelli, Chiara, Caruyer, Emmanuel, Chen, Hsin-Chin, Copeland, Christopher, Corouge, Isabelle, Cutini, Simone, Di Lorenzo, Renata, Dresler, Thomas, Eggebrecht, Adam T., Ehlis, Ann-Christine, Erdoğan, Sinem B., Evenblij, Danielle, Ferdous, Talukdar Raian, Fracalossi, Victoria, Franzén, Erika, Gallagher, Anne, Gerloff, Christian, Gervain, Judit, Goldhamer, Noy, Gossé, Louisa K., Guérin, Ségolène M. R., Guevara, Edgar, Hosseini, SM Hadi, Innes-Brown, Hamish, Int-Veen, Isabell, Jaffe-Dax, Sagi, Jégou, Nolwenn, Kawaguchi, Hiroshi, Kelsey, Caroline, Kent, Michaela, Kessler, Roman, Kherbawy, Nadeen, Klein, Franziska, Kochavi, Nofar, Kolisnyk, Matthew, Koren, Yogev, Kroczek, Agnes, Kvist, Alexander, Lin, Chen-Hao Paul, Löw, Andreas, Luan, Siying, Mao, Darren, Martins, Giovani G., Middell, Eike, Montero-Hernandez, Samuel, Mutlu, Murat Can, Novi, Sergio L., Paquette, Natacha, Paranawithana, Ishara, Parmet, Yisrael, Peelle, Jonathan E., Peng, Ke, Peng, Tommy, Pereira, João, Pinti, Paola, Pollonini, Luca, Jounghani, Ali Rahimpour, Reindl, Vanessa, Ringels, Wiebke, Schopp, Betti, Schulte, Alina, Schulte-Rüther, Martin, Segel, Ari, Ala, Tirdad Seifi, Shader, Maureen J., Shavit, Hadas, Sherafati, Arefeh, Soltanlou, Mojtaba, Sorger, Bettina, Speh, Emma, Stubbs, Kevin D., Stute, Katharina, Sullivan, Eileen F., Tak, Sungho, Tipado, Zeus, Tremblay, Julie, Vahidi, Homa, Van Eeckhoutte, Maaike, Vannasing, Phetsamone, Vergotte, Gregoire, Vincent, Marion A., Weiss, Eileen, Yang, Dalin, Yükselen, Gülnaz, Zapała, Dariusz and Zemanek, Vit
2025.
fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience.
Communications Biology
8
(1)
, 1149.
10.1038/s42003-025-08412-1
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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 |
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Date Type: | Published Online |
Status: | Published |
Schools: | Schools > Psychology |
Additional Information: | License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access |
Publisher: | Nature Research |
Date of First Compliant Deposit: | 12 August 2025 |
Date of Acceptance: | 18 June 2025 |
Last Modified: | 12 Aug 2025 10:46 |
URI: | https://orca.cardiff.ac.uk/id/eprint/180383 |
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