Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).

Article  PubMed  Google Scholar 

Yendiki, A., Koldewyn, K., Kakunoori, S., Kanwisher, N. & Fischl, B. Spurious group differences due to head motion in a diffusion MRI study. Neuroimage 88, 79–90 (2014).

Article  PubMed  Google Scholar 

Reuter, M. et al. Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage 107, 107–115 (2015).

Article  PubMed  Google Scholar 

Alexander-Bloch, A. et al. Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI. Hum. Brain Mapp. 37, 2385–2397 (2016).

Article  PubMed  Google Scholar 

Etzel, J. A. Efficient evaluation of the Open QC task fMRI dataset. Front. Neuroimag. 2, 1070274 (2023).

Article  Google Scholar 

Esteban, O. et al. MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites. PLoS ONE 12, e0184661 (2017).

Article  PubMed  PubMed Central  Google Scholar 

Esteban, O. et al. Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines. Sci. Data 6, 30 (2019).

Article  PubMed  PubMed Central  Google Scholar 

Botvinik-Nezer, R. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84–88 (2020).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Taylor, P. A. et al. Editorial: demonstrating quality control (QC) procedures in fMRI. Front. Neurosci. 17, 1205928 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Lepping, R. J. et al. Quality control in resting-state fMRI: the benefits of visual inspection. Front. Neurosci. 17, 1076824 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Provins, C., MacNicol, E., Seeley, S. H., Hagmann, P. & Esteban, O. Quality control in functional MRI studies with MRIQC AND fMRIPrep. Front. Neuroimag. 1, 1073734 (2023).

Article  Google Scholar 

Gardner, E. A. et al. Detection of degradation of magnetic resonance (MR) images: comparison of an automated MR image-quality analysis system with trained human observers. Acad. Radiol. 2, 277–281 (1995).

Article  CAS  PubMed  Google Scholar 

Woodard, J. P. & Carley-Spencer, M. P. No-reference image quality metrics for structural MRI. Neuroinformatics 4, 243–262 (2006).

Article  PubMed  Google Scholar 

Gedamu, E. L., Collins, D. L. & Arnold, D. L. Automated quality control of brain MR images. J. Magn. Reson. Imaging 28, 308–319 (2008).

Article  PubMed  Google Scholar 

Mortamet, B. et al. Automatic quality assessment in structural brain magnetic resonance imaging. Magn. Reson. Med. 62, 365–372 (2009).

Article  PubMed  PubMed Central  Google Scholar 

Shehzad, Z. et al. The Preprocessed Connectomes Project Quality Assessment Protocol—a resource for measuring the quality of MRI data. In Front. Neurosci. Conference Abstract: Neuroinformatics 2015 https://www.frontiersin.org/10.3389/conf.fnins.2015.91.00047/event_abstract (2015).

Pizarro, R. A. et al. Automated quality assessment of structural magnetic resonance brain images based on a supervised machine learning algorithm. Front. Neuroinform. 10, 52 (2016).

Article  PubMed  PubMed Central  Google Scholar 

Backhausen, L. L. et al. Quality control of structural MRI images applied using FreeSurfer—a hands-on workflow to rate motion artifacts. Front. Neurosci. 10, 558 (2016).

Article  PubMed  PubMed Central  Google Scholar 

Alfaro-Almagro, F. et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2017).

Article  PubMed  PubMed Central  Google Scholar 

White, T. et al. Automated quality assessment of structural magnetic resonance images in children: comparison with visual inspection and surface-based reconstruction. Hum. Brain Mapp. 39, 1218–1231 (2018).

Article  PubMed  Google Scholar 

Keshavan, A., Yeatman, J. & Rokem, A. Combining citizen science and deep learning to amplify expertise in neuroimaging. Front. Neuroinform. 13, 29 (2019).

Article  PubMed  PubMed Central  Google Scholar 

Klapwijk, E. T., van de Kamp, F., van der Meulen, M., Peters, S. & Wierenga, L. M. Qoala-T: a supervised-learning tool for quality control of FreeSurfer segmented MRI data. Neuroimage 189, 116–129 (2019).

Article  PubMed  Google Scholar 

Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data 3, 160044 (2016).

Article  PubMed  PubMed Central  Google Scholar 

Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5, 13 (2011).

Article  PubMed  PubMed Central  Google Scholar 

Esteban, O. et al. Analysis of task-based functional MRI data preprocessed with fMRIPrep. Nat. Protoc. 15, 2186–2202 (2020).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Poldrack, R. A. et al. The past, present, and future of the brain imaging data structure (BIDS). Imag. Neurosc. 2, imag-2-00103 (2024).

Article  Google Scholar 

MacNicol, E. E. et al. Extending MRIQC to rodents: image quality metrics for rat MRI. In Annual Meeting of the European Society for Molecular Imaging (EMIM) Vol. 17 PW23–PW913 (Thessaloniki, Greece, 2022).

Provins, C. et al. Removing facial features from structural MRI images biases visual quality assessment. PLoS Biol. 23, e3003149 (2025).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Niso, G. et al. Open and reproducible neuroimaging: from study inception to publication. Neuroimage 263, 119623 (2022).

Article  PubMed  PubMed Central  Google Scholar 

Gulban, O. F. et al. poldracklab/pydeface: PyDeface v2.0.2. Zenodo https://zenodo.org/records/6856482 (2022).

Bhalerao, G. V. et al. Systematic evaluation of the impact of defacing on quality and volumetric assessments on T1-weighted MR-images. J. Neuroradiol. 49, 250–257 (2022).

Article  PubMed  Google Scholar 

Nakua, H. et al. Systematic comparisons of different quality control approaches applied to three large pediatric neuroimaging datasets. Neuroimage 274, 120119 (2023).

Article  PubMed  Google Scholar 

Marcus, D. S. et al. Human Connectome Project informatics: quality control, database services, and data visualization. Neuroimage 80, 202–219 (2013).

Article  PubMed  PubMed Central  Google Scholar 

Keshavan, A., Yeatman, J. D. & Rokem, A. Combining citizen science and deep learning to amplify expertise in neuroimaging. Front. Neuroinform. 13, 29 (2019).

Article  PubMed  PubMed Central  Google Scholar 

Richie-Halford, A. et al. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci. Data 9, 616 (2022).

Article  PubMed  PubMed Central  Google Scholar 

Garcia, M., Dosenbach, N. & Kelly, C. BrainQCNet: a Deep Learning attention-based model for the automated detection of artifacts in brain structural MRI scans. Imaging Neurosci. (Camb.) 2, 1–16 (2024).

Google Scholar 

Sanchez, T. et al. FetMRQC: a robust quality control system for multi-centric fetal brain MRI. Med. Image Anal. 97, 103282 (2024).

Article