ArticleNature communications2024
Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
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- Task and Resting-State Functional Magnetic Resonance Imaging Modeling of Brain-Behavior Relationships in Developmental Cohorts.Biological psychiatry · 2026Review
- Functional brain growth trajectories across the first decade of life from a single longitudinal cohort.Research square · 2026Article
- Benchmarking resting state fMRI connectivity pipelines for classification: robust accuracy despite processing variability in cross-site eye state prediction.Brain informatics · 2026Article
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- Deciphering multiway multiscale brain network connectivity from birth to 6 months.Communications biology · 2026Article
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- Magnetic Resonance Imaging-Guided Neuronavigation for Transcranial Magnetic Stimulation in Mood Disorders: Technical Foundation, Advances, and Emerging Tools.Human brain mapping · 2025Review
- The Use of Repetitive Transcranial Magnetic Stimulation to Improve Cognitive Impairment in Patients With Stroke Based on rs-fMRI Findings: Protocol for a Meta-Analysis.JMIR research protocols · 2025Article
- Subcortical Hubs of Brain Networks Sustaining Human Consciousness.Human brain mapping · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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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