fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience
Meryem A. Yücel, Robert Luke, Rickson C. Mesquita, Alexander von Lühmann, David M. A. Mehler, Michael Lührs, Jessica Gemignani, Androu Abdalmalak, Franziska Albrecht, Iara de Almeida Ivo, Christina Artemenko, Kira Ashton, Paweł Augustynowicz, Aahana Bajracharya, Élise Bannier, Beatrix Barth, Laurie Bayet, Jacqueline Behrendt, Hadi Borjkhani, Lenaic Borot, Jordan A. Borrell, Sabrina Brigadoi, Kolby Brink, Chiara Bulgarelli, Emmanuel Caruyer, Hsin‐Chin Chen, Christopher Copeland, Isabelle Corouge, Simone Cutini, Renata Di Lorenzo, Thomas Dresler, Adam T. Eggebrecht, Ann‐Christine Ehlis, Sinem Burcu Erdoğan, Daniëlle Evenblij, Talukdar Raian Ferdous, Victoria Fracalossi, Erika Franzén, Anne Gallagher, Christian Gerloff, Judit Gervain, Noy Goldhamer, Louisa K. Gossé, Ségolène M. R. Guérin, Edgar Guevara, S. M. Hadi Hosseini, Hamish Innes-Brown, Isabell Int-Veen, Sagi Jaffe‐Dax, Nolwenn Jégou, Hiroshi Kawaguchi, Caroline Kelsey, M. Kent, Roman Kessler, Nadeen Kherbawy, Franziska Klein, Nofar Kochavi, Matthew Kolisnyk, Yogev Koren, Agnes Kroczek, Alexander Kvist, Chen-Hao P. Lin, Andreas Löw, Siying Luan, Darren Mao, Giovani Grisotti Martins, Eike Middell, Samuel Montero‐Hernández, Murat Can Mutlu, Sergio L. Novi, Natacha Paquette, Ishara Paranawithana, Yisrael Parmet, Jonathan E. Peelle, Ke Peng, Tommy Peng, João Pereira, Paola Pinti, Luca Pollonini, Ali Rahimpour Jounghani, Vanessa Reindl, Wiebke Ringels, Betti Schopp, Alina Schulte, Martin Schulte‐Rüther, Ari Segel, Tirdad Seifi, Maureen J. Shader, Hadas Shavit, Arefeh Sherafati, Mojtaba Soltanlou, Bettina Sorger, Emma Speh, Kevin Stubbs, Katharina Stute, Eileen Sullivan, Sungho Tak, Zeus Tipado, Julie Tremblay, Homa Vahidi
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.