fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience
Yücel, Meryem A. and Luke, Robert and Mesquita, Rickson C. and Von Lühmann, Alexander and Mehler, David M. A. and Lührs, Michael and Gemignani, Jessica and Abdalmalak, Androu and Albrecht, Franziska and De Almeida Ivo, Iara and Artemenko, Christina and Ashton, Kira and Augustynowicz, Paweł and Bajracharya, Aahana and Bannier, Elise and Barth, Beatrix and Bayet, Laurie and Behrendt, Jacqueline and Khani, Hadi Borj and Borot, Lenaic and Borrell, Jordan A. and Brigadoi, Sabrina and Brink, Kolby and Bulgarelli, Chiara and Caruyer, Emmanuel and Chen, Hsin-Chin and Copeland, Christopher and Corouge, Isabelle and Cutini, Simone and Di Lorenzo, Renata and Dresler, Thomas and Eggebrecht, Adam T. and Ehlis, Ann-Christine and Erdoğan, Sinem B. and Evenblij, Danielle and Ferdous, Talukdar Raian and Fracalossi, Victoria and Franzén, Erika and Gallagher, Anne and Gerloff, Christian and Gervain, Judit and Goldhamer, Noy and Gossé, Louisa K. and Guérin, Ségolène M. R. and Guevara, Edgar and Hosseini, Sm Hadi and Innes-Brown, Hamish and Int-Veen, Isabell and Jaffe-Dax, Sagi and Jégou, Nolwenn and Kawaguchi, Hiroshi and Kelsey, Caroline and Kent, Michaela and Kessler, Roman and Kherbawy, Nadeen and Klein, Franziska and Kochavi, Nofar and Kolisnyk, Matthew and Koren, Yogev and Kroczek, Agnes and Kvist, Alexander and Lin, Chen-Hao Paul and Löw, Andreas and Luan, Siying and Mao, Darren and Martins, Giovani G. and Middell, Eike and Montero-Hernandez, Samuel and Mutlu, Murat Can and Novi, Sergio L. and Paquette, Natacha and Paranawithana, Ishara and Parmet, Yisrael and Peelle, Jonathan E. and Peng, Ke and Peng, Tommy and Pereira, João and Pinti, Paola and Pollonini, Luca and Jounghani, Ali Rahimpour and Reindl, Vanessa and Ringels, Wiebke and Schopp, Betti and Schulte, Alina and Schulte-Rüther, Martin and Segel, Ari and Ala, Tirdad Seifi and Shader, Maureen J. and Shavit, Hadas and Sherafati, Arefeh and Soltanlou, Mojtaba and Sorger, Bettina and Speh, Emma and Stubbs, Kevin D. and Stute, Katharina and Sullivan, Eileen F. and Tak, Sungho and Tipado, Zeus and Tremblay, Julie and Vahidi, Homa and Van Eeckhoutte, Maaike and Vannasing, Phetsamone and Vergotte, Gregoire and Vincent, Marion A. and Weiss, Eileen and Yang, Dalin and Yükselen, Gülnaz and Zapała, Dariusz and Zemanek, Vit
Communications Biology
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 80of 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.
August / 2025
article
1149
DAIsy
Developing AI ecosystems improving diagnosis and care of mental diseases
Developing AI ecosystems improving diagnosis and care of mental diseases
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