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 · 2024 · Nature Communications
Paper
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.
Analysis
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.
Discovery
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