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Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics
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DOI：10.1038/s41467-024-48781-5.png)
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Abstract
En 中文
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. The effects of different choices on preprocessing pipelines for functional connectomics remain unclear. Here, the authors systematically evaluate a multitude of pipelines on resting-state fMRI, revealing a number of optimal pipelines for functional brain network analysis.
Keywords:
TEST-RETEST RELIABILITY
RESTING-STATE FMRI
INDEPENDENT COMPONENT ANALYSIS
BRAIN NETWORKS
MOTION CORRECTION
GLOBAL SIGNAL
GRAPH METRICS
CONNECTIVITY
PARCELLATION
STRATEGIES
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