Moving beyond processing- and analysis-related variation in resting-state functional brain imaging

Xinhui Li, Nathalia Bianchini Esper, Lei Ai, Steve Giavasis, Hecheng Jin, Eric Feczko, Ting Xu, Jon Clucas, Alexandre Franco, Anibal Sólon Heinsfeld, Azeez Adebimpe, Joshua T. Vogelstein, Chao-Gan Yan, Oscar Esteban, Russell A. Poldrack, Cameron Craddock, Damien Fair, Theodore Satterthwaite, Gregory Kiar and Michael P. Milham ()
Additional contact information
Xinhui Li: Child Mind Institute
Nathalia Bianchini Esper: Child Mind Institute
Lei Ai: Child Mind Institute
Steve Giavasis: Child Mind Institute
Hecheng Jin: Child Mind Institute
Eric Feczko: University of Minnesota
Ting Xu: Child Mind Institute
Jon Clucas: Child Mind Institute
Alexandre Franco: Child Mind Institute
Anibal Sólon Heinsfeld: The University of Texas at Austin
Azeez Adebimpe: University of Pennsylvania
Joshua T. Vogelstein: Johns Hopkins University
Chao-Gan Yan: Institute of Psychology
Oscar Esteban: Lausanne University Hospital and University of Lausanne
Russell A. Poldrack: Stanford University
Cameron Craddock: The University of Texas at Austin
Damien Fair: University of Minnesota
Theodore Satterthwaite: University of Pennsylvania
Gregory Kiar: Child Mind Institute
Michael P. Milham: Child Mind Institute

Nature Human Behaviour, 2024, vol. 8, issue 10, 2003-2017

Abstract: Abstract When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging. We show that, even when handling identical data, interpipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low interpipeline agreement can go unrecognized until the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially we show that, when interpipeline agreement is compromised, so too is the consistency of insights from brain-wide association studies. We highlight the importance of comparing analytic configurations, because both widely discussed and commonly overlooked decisions can lead to marked variation.

Date: 2024
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DOI: 10.1038/s41562-024-01942-4

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