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

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.

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Publication Details

Journal
Communications Biology
Published
2025-08-04
DOI
https://doi.org/10.1038/s42003-025-08412-1
Citations
11
Primary Topic
Optical Imaging and Spectroscopy Techniques
Type
article
Field-Weighted Citation Impact
14.96

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article

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

Ségolène M. R. Guérin, Hadi Borjkhani, Arefeh Sherafati, Erika Franzén et al.
11 citations
Communications Biology
Optical Imaging and Spectroscopy Techniques
14.96
article

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

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

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.

Communications BiologyVol. 8(1)
Boston University (US), John Paul II Catholic University of Lublin (PL), HTW Berlin - University of Applied Sciences (DE), University of Nebraska at Omaha (US), Ben-Gurion University of the Negev (IL), Western University (CA), Boston Children's Hospital (US), Northeastern University (US), Centre National de la Recherche Scientifique (FR), Bionics Institute (AU), Carl von Ossietzky Universität Oldenburg (DE), MEF University (TR), Karolinska University Hospital (SE), Harvard University (US), University of Padua (IT), National Chung Cheng University (TW), Inserm (FR), University of California, Merced (US), Tel Aviv University (IL), Nanyang Technological University (SG), University of California, San Francisco (US), Universidade Estadual de Campinas (UNICAMP) (BR), American University (US), Helmut Schmidt University (DE), Chungnam National University (KR), Autonomous University of San Luis Potosí (MX), Université de Montpellier (FR), Oldenburger Institut für Informatik (DE), Washington University in St. Louis (US), Purdue University West Lafayette (US), Heidelberg University (DE), University of Münster (DE), University of Johannesburg (ZA), Chemnitz University of Technology (DE), London Health Sciences Centre (CA), University Hospital Heidelberg (DE), Institut de Recherche en Informatique et Systèmes Aléatoires (FR), Rigshospitalet (DK), Karolinska Institutet (SE), University of Surrey (GB), University of Massachusetts Boston (US), Maastricht University (NL), Medizinische Hochschule Hannover (DE), Berliner Institut für Sozialforschung (DE), NeoPhotonics (United States) (US), Australian Hearing (AU), Mallinckrodt (United States) (US), Universitätsmedizin Göttingen (DE), Universitätsklinikum Aachen (DE), IMT Mines Alès (FR), University of Kansas Medical Center (US), Max Planck Institute for Human Cognitive and Brain Sciences (DE), Oticon Medical (Denmark) (DK), Jülich Aachen Research Alliance (DE), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE), Korea Basic Science Institute (KR), Kent Hastanesi (TR), Centre Hospitalier Universitaire de Rennes (FR), German Center for Diabetes Research (DE), Sciences Cognitives et Sciences Affectives (FR), Empenn (FR), EuroMov Digital Health in Motion, University of Houston (US), Technische Universität Berlin (DE), University of Manitoba (CA), Université de Rennes (FR), Liverpool John Moores University (GB), Université de Montréal (CA), National Institute of Advanced Industrial Science and Technology (JP), University of Coimbra (PT), Cardiff University (GB), University of Birmingham (GB), University of Tübingen (DE), University of Amsterdam (NL), RWTH Aachen University (DE), UCLouvain (BE), Otto-von-Guericke-Universität Magdeburg (DE), Technical University of Denmark (DK), Stanford University (US), Birkbeck, University of London (GB), Macquarie University (AU)
National Science Foundation, National Alliance for Research on Schizophrenia and Depression, Australian Government, Leverhulme Trust, Fundação de Amparo à Pesquisa do Estado de São Paulo, Bundesministerium für Bildung und Forschung, Ministero della Salute, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Garnett Passe and Rodney Williams Memorial Foundation, Karolinska Institutet, Conseil Régional de Bretagne, Norrbacka-Eugeniastiftelsen, Ministero dell'Università e della Ricerca, National Institutes of Health, Fundação para a Ciência e a Tecnologia, National Institute of Child Health and Human Development, HORIZON EUROPE European Innovation Council
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Optical Imaging and Spectroscopy Techniques
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