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Siying Luan

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Articles

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

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By Meryem A. Yücel, Robert Luke, Rickson C. Mesquita, Alexander Von Luhmann, Christina Artemenko, Aahana Bajracharya, Elise Bannier, Adam T. Eggebrecht, Noy Goldhamer, Edgar Guevara, Sagi Jaffe-Dax, Roman Kessler, Yogev Koren, Chen-Hao Paul Lin, Darren Mao, Murat Mutlu, Ishara Paranawithana, Jonathan E. Peelle, Alina Schulte, Bettina Sorger, Kevin D. Stubbs, Eileen Sullivan, Sungho Tak, Marion Vincent, Laurie Bayet, Hadi Borj Khani, Renata Di Lorenzo, SM Hadi Hosseini, Alexander Kvist, Siying Luan, Maureen J. Shader, Katharina Stute, Maaike Van Eeckhoutte
| Nature Verified
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.
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