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Related Experiment Video

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Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics.

Andrea I Luppi1,2,3,4, Helena M Gellersen5,6, Zhen-Qi Liu7

  • 1Division of Anaesthesia, University of Cambridge, Cambridge, UK. al857@cam.ac.uk.

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|June 4, 2024
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Summary

Choosing the right data-processing pipeline is crucial for accurate brain network analysis. This study identifies optimal pipelines for resting-state functional MRI to ensure reliable functional connectomics research.

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Area of Science:

  • Neuroscience
  • Network Science
  • Data Science

Background:

  • Functional interactions between brain regions are studied using network science.
  • Resting-state functional MRI (fMRI) is a key tool for mapping brain networks.

Purpose of the Study:

  • To systematically evaluate 768 data-processing pipelines for resting-state fMRI network reconstruction.
  • To identify pipelines that minimize motion artifacts and improve test-retest reliability.
  • To find pipelines sensitive to inter-subject and experimental variations.

Main Methods:

  • Evaluation of 768 pipelines based on brain parcellation, connectivity definition, and global signal regression.
  • Criteria included minimizing motion confounds and test-retest discrepancies.
  • Assessment of sensitivity to inter-subject differences and experimental effects.

Main Results:

  • Vast and systematic variability found in pipeline suitability for functional connectomics.
  • Most pipelines failed at least one criterion, leading to potentially misleading results.
  • A subset of optimal pipelines consistently met all criteria across diverse datasets and time scales.

Conclusions:

  • The choice of data-processing pipeline significantly impacts functional connectomics findings.
  • Optimal pipelines exist that ensure robust and reliable brain network analysis.
  • Recommendations are provided to guide future best practices in the field.