ArticleGigaScience2021
Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses.
Article in GigaScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- Multiclass machine learning models for molecular subtype identification of pediatric low-grade glioma using bi-institutional MRIs for precision medicine.NPJ precision oncology · 2025Article
- Choice of Processing Pipelines for T1-Weighted Brain MRI Impacts Association and Prediction Analyses.Human brain mapping · 2025Article
- Brain Age Prediction: Deep Models Need a Hand to Generalize.Human brain mapping · 2025Article
- When no answer is better than a wrong answer: A causal perspective on batch effects.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Open-source platforms to investigate analytical flexibility in neuroimaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Regional CSF volume quantification using deep learning for comparative analysis of brain atrophy in frontotemporal dementia subtypes.Frontiers in aging neuroscience · 2025Article
- Pipeline variability: Friend or foe, depending on the goal: Comment on Germani et al. 2025.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- HED LANG - A Hierarchical Event Descriptors library extension for annotation of language cognition experiments.Scientific data · 2024Article
- Domain adaptation in small-scale and heterogeneous biological datasets.Science advances · 2024Review
- Why experimental variation in neuroimaging should be embraced.Nature communications · 2024Review
- Moving beyond processing- and analysis-related variation in resting-state functional brain imaging.Nature human behaviour · 2024Article
- Sex classification from functional brain connectivity: Generalization to multiple datasets.Human brain mapping · 2024Article
- Sex classification from functional brain connectivity: Generalization to multiple datasets Generalizability of sex classifiers.bioRxiv : the preprint server for biology · 2024Article
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13 authors.
Funding
Abstract
backgroundThe choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance.
methodsHere we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction.
resultsResults show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks.
conclusionsThis comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.
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