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 · 2025 · Communications Biology
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
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 dif
Abstract by Meryem A. Yücel; Robert Luke; Rickson C. Mesquita; Alexander von Lühmann; David M. A. Mehler; Michael Lührs; Jessica Gemignani; Androu Abdalmalak, Communications Biology (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s42003-025-08412-1