ArticleImaging neuroscience (Cambridge, Mass.)2024
A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth, and interactive QC with afni_proc.py and more.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Retinotopic coding organizes the interaction between internally and externally oriented brain networks.eLife · 2026Article
- Article
- Article
- The Bias Associated with Movement-related Data Exclusion in Psychosis MRI Research.Indian journal of psychological medicine · 2025Article
- The human brain mechanisms of afterimages: From networks to cortical layers.bioRxiv : the preprint server for biology · 2025Article
- MRIcroGL: voxel-based visualization for neuroimaging.Nature methods · 2025Article
- From MRIcro to MRIcron: The evolution of neuroimaging visualization tools.Neuropsychologia · 2025Article
- Processing, evaluating, and understanding FMRI data with afni_proc.py.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Article
Corrections and comments
- Update of
Authors and funding
9 authors.
Funding
Abstract
Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically underdiscussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single-subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool,
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.