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  1. Home
  3. Statistical Inference For Same Data Meta-analysis In Neuroimaging Multiverse Analyzes
  1. Home
  3. Statistical Inference For Same Data Meta-analysis In Neuroimaging Multiverse Analyzes

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Statistical inference for same data meta-analysis in neuroimaging multiverse analyzes

Jeremy Lefort-Besnard1, Thomas E Nichols2, Camille Maumet1

  • 1Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France.

Imaging Neuroscience (Cambridge, Mass.)
|August 13, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces same data meta-analysis (SDMA) to address reproducibility issues in neuroimaging. SDMA methods effectively analyze multiverse outputs, accounting for inter-pipeline dependence in functional magnetic resonance imaging (fMRI) data.

Keywords:
multiverse analysisreproducibilitysame data meta-analysisstatistical inferencetask-fMRI

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Area of Science:

  • Neuroimaging analysis
  • Computational neuroscience
  • Statistical modeling

Background:

  • Task-functional magnetic resonance imaging (fMRI) research utilizes numerous analysis tools.
  • Varied analytical approaches can inflate false positive rates and hinder reproducibility in neuroimaging.
  • Multiverse analyses explore pipeline variations but create challenges in interpreting multiple outputs from a single dataset.

Purpose of the Study:

  • To develop and validate methods for meta-analysis in the context of multiverse analyses, specifically addressing inter-pipeline dependence.
  • To introduce "same data meta-analysis" (SDMA) as a solution for extracting consensus inferences from multiple analytical outputs derived from a single dataset.
  • To provide reliable tools for interpreting findings from complex neuroimaging studies.

Main Methods:

  • Developed a suite of same data meta-analysis (SDMA) methods to account for inter-pipeline dependence in multiverse outputs.
  • Assessed the validity of SDMA methods through simulations.
  • Tested SDMA models on real-world multiverse analysis outputs from the NARPS and HCP Young Adult studies.

Main Results:

  • The proposed SDMA models demonstrate validity in the presence of inter-pipeline dependence.
  • Simulations confirmed the effectiveness of the developed SDMA methods.
  • Real-world application on NARPS and HCP Young Adult datasets showed the practical utility of SDMA.

Conclusions:

  • Same data meta-analysis (SDMA) provides a robust framework for analyzing dependent results from multiverse neuroimaging studies.
  • The validated SDMA methods offer researchers reliable options for drawing consensus inferences from complex analytical pipelines.
  • This work enhances the interpretability and reproducibility of neuroimaging findings generated through multiverse analyses.