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

Nature Human Behaviour (2024)Cite this article

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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.

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Fig. 1: Interpipeline agreement for minimal preprocessing in five fMRI preprocessing packages.
Fig. 2: Minimal preprocessing comparisons of C-PAC harmonized pipelines.
Fig. 3: Impact of interpipeline agreement on result consistency.
Fig. 4: Impact of scan duration and GSR on minimal preprocessing results from C-PAC harmonized pipelines.
Fig. 5: Pairwise identification of sources of variation across harmonized pipelines.
Fig. 6: Impact of MNI152 template version and write-out resolution on functional connectomes.

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Data availability

The data supporting this study’s findings are publicly available. We used the HNU test–retest dataset (https://fcon_1000.projects.nitrc.org/indi/CoRR/html/hnu_1.html) made available via the Consortium for Reliability and Reproducibility and Healthy Brain Network dataset (https://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/).

Code availability

All software created and used in this project is publicly available. The C-PAC pipeline is released under a BSD 3-clause licence and is available on GitHub at https://github.com/FCP-INDI/C-PAC/releases/tag/v1.8.5; the ABCD–BIDS pipeline is released under a BSD 3-clause licence and is available on GitHub at https://github.com/DCAN-Labs/abcd-hcp-pipeline/releases/tag/v0.0.3; the CCS pipeline is available on GitHub at https://github.com/zuoxinian/CCS; the fMRIPrep–LTS pipeline is released under Apache Licence 2.0 and is available on GitHub at https://github.com/nipreps/fmriprep/releases/tag/20.2.1. All templates were accessed through TemplateFlow45. All analysis software, including experiments and figure generation, is available on GitHub at https://github.com/XinhuiLi/PipelineAgreement and on Zenodo at https://zenodo.org/badge/latestdoi/415936717 (ref. 72). The preprocessed functional connectivity data can be found on OSF at https://osf.io/kgpu2/. In the completion of this work we used the following versions of critically relevant software: Matlab (2014a), AFNI (21.1.00), FSL (6.0), ANTs (2.3.3.dev168-g29bdf), FreeSurfer (6.0.0), SPM (12) and C-PAC (1.8.5).

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Acknowledgements

This work was supported in part by gifts from J. P. Healey, P. Green and R. Cowen to the Child Mind Institute. In addition, grant awards were received from the NIH BRAIN Initiative to M.P.M. and C.C. (no. R24 MH11480602), to G.K. and M.P.M. (no. RF1MH130859) and to R.A.P., O.E., M.P.M. and T.S. (no. RF1MH121867); and from NIMH to T.S. and M.P.M. (no. R01MH120482). O.E. received support from SNSF Ambizione project no. 185872. C.-G.Y. received support from the National Natural Science Foundation of China (grant nos. 82122035, 81671774 and 81630031).

Author information

Author notes

  1. These authors contributed equally: Xinhui Li, Nathalia Bianchini Esper, Gregory Kiar, Michael P. Milham.

Authors and Affiliations

  1. Child Mind Institute, New York, NY, USA

    Xinhui Li, Nathalia Bianchini Esper, Lei Ai, Steve Giavasis, Hecheng Jin, Ting Xu, Jon Clucas, Alexandre Franco, Gregory Kiar & Michael P. Milham

  2. Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA

    Eric Feczko & Damien Fair

  3. Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA

    Eric Feczko & Damien Fair

  4. Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA

    Eric Feczko

  5. Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA

    Eric Feczko & Damien Fair

  6. Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA

    Alexandre Franco & Michael P. Milham

  7. Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA

    Anibal Sólon Heinsfeld & Cameron Craddock

  8. Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Azeez Adebimpe & Theodore Satterthwaite

  9. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Joshua T. Vogelstein

  10. CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China

    Chao-Gan Yan

  11. Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

    Chao-Gan Yan

  12. International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, China

    Chao-Gan Yan

  13. Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

    Chao-Gan Yan

  14. Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

    Oscar Esteban

  15. Department of Psychology, Stanford University, San Francisco, CA, USA

    Oscar Esteban & Russell A. Poldrack

  16. Institute of Child Development, University of Minnesota, Minneapolis, MN, USA

    Damien Fair

  17. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Theodore Satterthwaite

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Contributions

G.K. and M.P.M. conceptualized the study design, supervised work, wrote the first draft of the manuscript and oversaw all revisions. X.L., N.B.E., L.A. and G.K. wrote the preprocessing, data analysis and computational model scripts. J.C. reviewed the code. A.S.H. and S.G. provided technical support. X.L., N.B.E., G.K. and M.P.M. wrote the first draft of the manuscript. All authors reviewed the manuscript and contributed to the discussion and results.

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Correspondence to Michael P. Milham.

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Li, X., Bianchini Esper, N., Ai, L. et al. Moving beyond processing- and analysis-related variation in resting-state functional brain imaging. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01942-4

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