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.; 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.; Erdoğan, 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. H.; Innes-Brown, H.; Int-Veen, I.; Jaffe-Dax, S.; Jégou, N.; Kawaguchi, H.; Kelsey, C.; Kent, M.; Kessler, R.; Kherbawy, N.; Klein, F.; Kochavi, N.; Kolisnyk, M.; Koren, Y.; Kroczek, A.; Kvist, A.; Lin, C. H. P.; Löw, A.; Luan, S.; Mao, D.; Martins, G. G.; Middell, E.; Montero-Hernandez, S.; Mutlu, M. C.; Novi, S. L.; Paquette, N.; Paranawithana, I.; Parmet, Y.; Peelle, J. E.; Peng, K.; Peng, T.; Pereira, J.; Pinti, P.; Pollonini, L.; Jounghani, A. R.; Reindl, V.; Ringels, W.; Schopp, B.; Schulte, A.; Schulte-Rüther, M.; Segel, A.; Ala, T. S.; Shader, M. J.; Shavit, H.; Sherafati, A.; Soltanlou, M.; Sorger, B.; Speh, E.; Stubbs, K. D.; Stute, K.; Sullivan, E. F.; Tak, S.; Tipado, Z.; Tremblay, J.; Vahidi, H.

doi:10.1038/s42003-025-08412-1

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...