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; Pawel Augustynowicz; Aahana Bajracharya; Elise Bannier; Beatrix Barth; Laurie Bayet; Jacqueline Behrendt; Hadi Borj Khani; 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 B Erdogan; Danielle 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; João Figueiredo Pereira; Bettina Sorger; Et al.

doi:10.1038/s42003-025-08412-1

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, Pawel Augustynowicz, Aahana Bajracharya, Elise Bannier, Beatrix Barth, Laurie Bayet, Jacqueline Behrendt, Hadi Borj Khani, Lenaic BorotJordan 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 B Erdogan, Danielle 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, João Figueiredo Pereira, Bettina Sorger, Et al.

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Article number	1149
Number of pages	17
Journal	Communications Biology
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s42003-025-08412-1
Publication status	Published - 4 Aug 2025

Keywords

Spectroscopy, Near-Infrared/methods standards
Reproducibility of Results
Humans
Data Accuracy
Brain/diagnostic imaging physiology
Research Personnel

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10.1038/s42003-025-08412-1Licence: CC BY

1 Erratum / corrigendum

Publisher Correction: fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience
Yücel, M. A., Luke, R., Mesquita, R. C., von Lühmann, A., Mehler, D. M. A., Lührs, M., Gemignani, J., Abdalmalak, A., Albrecht, F., de Almeida Ivo, I., Artemenko, C., Ashton, K., Augustynowicz, P., Bajracharya, A., Bannier, E., Barth, B., Bayet, L., Behrendt, J., Khani, H. B. & Borot, L. & 33 others, Borrell, J. A., Brigadoi, S., Brink, K., Bulgarelli, C., Caruyer, E., Chen, H.-C., Copeland, C., Corouge, I., Cutini, S., Di Lorenzo, R., Dresler, T., Eggebrecht, A. T., Ehlis, A.-C., Erdogan, S. B., Evenblij, D., Ferdous, T. R., Fracalossi, V., Franzén, E., Gallagher, A., Gerloff, C., Gervain, J., Goldhamer, N., Gossé, L. K., Guérin, S. M. R., Guevara, E., Hosseini, S. M. H., Innes-Brown, H., Int-Veen, I., Jaffe-Dax, S., Jégou, N., Figueiredo Pereira, J., Sorger, B. & Et al., 21 Aug 2025, In: Communications Biology. 8, 1, 1256.
Research output: Contribution to journal › Erratum / corrigendum › Academic

Open Access

Cite this

Yücel, M. A., Luke, R., Mesquita, R. C., von Lühmann, A., Mehler, D. M. A., Lührs, M., Gemignani, J., Abdalmalak, A., Albrecht, F., de Almeida Ivo, I., Artemenko, C., Ashton, K., Augustynowicz, P., Bajracharya, A., Bannier, E., Barth, B., Bayet, L., Behrendt, J., Khani, H. B., ... Et al. (2025). fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience. Communications Biology, 8(1), Article 1149. https://doi.org/10.1038/s42003-025-08412-1

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

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.",

keywords = "Spectroscopy, Near-Infrared/methods standards, Reproducibility of Results, Humans, Data Accuracy, Brain/diagnostic imaging physiology, Research Personnel",

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Yücel, MA, Luke, R, Mesquita, RC, von Lühmann, A, Mehler, DMA, Lührs, M, Gemignani, J, Abdalmalak, A, Albrecht, F, de Almeida Ivo, I, Artemenko, C, Ashton, K, Augustynowicz, P, Bajracharya, A, Bannier, E, Barth, B, Bayet, L, Behrendt, J, Khani, HB, Borot, L, Borrell, JA, Brigadoi, S, Brink, K, Bulgarelli, C, Caruyer, E, Chen, H-C, Copeland, C, Corouge, I, Cutini, S, Di Lorenzo, R, Dresler, T, Eggebrecht, AT, Ehlis, A-C, Erdogan, SB, Evenblij, D, Ferdous, TR, Fracalossi, V, Franzén, E, Gallagher, A, Gerloff, C, Gervain, J, Goldhamer, N, Gossé, LK, Guérin, SMR, Guevara, E, Hosseini, SMH, Innes-Brown, H, Int-Veen, I, Jaffe-Dax, S, Jégou, N, Figueiredo Pereira, J , Sorger, B & Et al. 2025, 'fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience', Communications Biology, vol. 8, no. 1, 1149. https://doi.org/10.1038/s42003-025-08412-1

TY - JOUR

T1 - fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience

AU - Yücel, Meryem A

AU - Luke, Robert

AU - Mesquita, Rickson C

AU - von Lühmann, Alexander

AU - Mehler, David M A

AU - Lührs, Michael

AU - Gemignani, Jessica

AU - Abdalmalak, Androu

AU - Albrecht, Franziska

AU - de Almeida Ivo, Iara

AU - Artemenko, Christina

AU - Ashton, Kira

AU - Augustynowicz, Pawel

AU - Bajracharya, Aahana

AU - Bannier, Elise

AU - Barth, Beatrix

AU - Bayet, Laurie

AU - Behrendt, Jacqueline

AU - Khani, Hadi Borj

AU - Borot, Lenaic

AU - Borrell, Jordan A

AU - Brigadoi, Sabrina

AU - Brink, Kolby

AU - Bulgarelli, Chiara

AU - Caruyer, Emmanuel

AU - Chen, Hsin-Chin

AU - Copeland, Christopher

AU - Corouge, Isabelle

AU - Cutini, Simone

AU - Di Lorenzo, Renata

AU - Dresler, Thomas

AU - Eggebrecht, Adam T

AU - Ehlis, Ann-Christine

AU - Erdogan, Sinem B

AU - Evenblij, Danielle

AU - Ferdous, Talukdar Raian

AU - Fracalossi, Victoria

AU - Franzén, Erika

AU - Gallagher, Anne

AU - Gerloff, Christian

AU - Gervain, Judit

AU - Goldhamer, Noy

AU - Gossé, Louisa K

AU - Guérin, Ségolène M R

AU - Guevara, Edgar

AU - Hosseini, S M Hadi

AU - Innes-Brown, Hamish

AU - Int-Veen, Isabell

AU - Jaffe-Dax, Sagi

AU - Jégou, Nolwenn

AU - Figueiredo Pereira, João

AU - Sorger, Bettina

AU - Et al.

PY - 2025/8/4

Y1 - 2025/8/4

N2 - 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.

AB - 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.

KW - Spectroscopy, Near-Infrared/methods standards

KW - Reproducibility of Results

KW - Humans

KW - Data Accuracy

KW - Brain/diagnostic imaging physiology

KW - Research Personnel

U2 - 10.1038/s42003-025-08412-1

DO - 10.1038/s42003-025-08412-1

M3 - Article

SN - 2399-3642

VL - 8

JO - Communications Biology

JF - Communications Biology

IS - 1

M1 - 1149

ER -

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

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

Cite this