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

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Abstract

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.

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This study systematically evaluates 768 fMRI data-processing pipelines to identify optimal methods for functional connectomics that minimize motion confounds and spurious discrepancies while remaining sensitive to inter-subject and experimental differences.

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Publication metadata

Authors: Andrea I. Luppi; Helena M. Gellersen; Zhen-Qi Liu; Alexander R. D. Peattie; Anne E. Manktelow; R. Adapa; Adrian M. Owen; Lorina Naçi; David Menon; Stavros I. Dimitriadis; Emmanuel A. Stamatakis
Published: Jun 4, 2024
Journal or venue: Nature Communications
DOI: 10.1038/s41467-024-48781-5
Paper type: journal article
Source: Openalex

DOI OpenAlex