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On the validity of fMRI mega-analyses using data processed with different pipelines.

Elodie Germani1, Xavier Rolland1, Pierre Maurel1

  • 1Univ Rennes, Inria, CNRS, Inserm, Rennes, France.

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Summary
This summary is machine-generated.

Analytical variability in functional magnetic resonance imaging (fMRI) data processing can lead to false positives. Combining fMRI data from different analysis pipelines without accounting for variations inflates detection rates.

Keywords:
analytical variabilitydata re-useneuroimagingpipelinesvalidity

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Area of Science:

  • Neuroimaging
  • Functional Magnetic Resonance Imaging (fMRI)
  • Data Science

Background:

  • Publicly available neuroimaging data, particularly from functional magnetic resonance imaging (fMRI), are valuable for increasing study sample sizes and improving robustness.
  • Functional magnetic resonance imaging (fMRI) studies typically preprocess raw data using a consistent analysis pipeline to generate subject-level contrast maps for group analysis.

Purpose of the Study:

  • To investigate the impact of analytical variability, arising from different data processing pipelines, on the reliability of mega-analyses combining functional magnetic resonance imaging (fMRI) data.
  • To determine if unaddressed differences in fMRI analysis workflows can induce false positive findings in large-scale data aggregation.

Main Methods:

  • Utilized the Human Connectome Project (HCP) multi-pipeline dataset, comprising contrast maps from 1,080 participants.
  • Processed the HCP dataset using 24 distinct analysis pipelines.
  • Conducted between-groups analyses comparing contrast maps generated by different pipelines to estimate pipeline-induced detection rates.

Main Results:

  • Analytical variability stemming from diverse fMRI processing pipelines can significantly impact results when data are combined.
  • Failure to account for differences in analysis workflows leads to an inflation of false positive rates in mega-analyses.
  • The study demonstrated that using data processed with different pipelines without harmonization inflates false positive detections.

Conclusions:

  • Analytical variability in functional magnetic resonance imaging (fMRI) data processing is a critical issue that must be addressed.
  • Unaccounted-for differences in fMRI analysis pipelines can compromise the validity of findings in large-scale data integration efforts.
  • Future mega-analyses should implement strategies to harmonize or account for variations in data processing pipelines to ensure accurate and reliable results.