Evidence map›Paper›PMID 38585923›Full record

ArticlebioRxiv : the preprint server for biology2024

A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth and interactive QC with afni_proc.py and more.

Paul A Taylor, Daniel R Glen, Gang Chen, Robert W Cox, Taylor Hanayik, Chris Rorden, Dylan M Nielson, Justin K Rajendra, Richard C Reynolds

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Paul A TaylorScientific and Statistical Computing Core, NIMH, NIH, USA.ORCID 0000-0002-7423-0090
Daniel R GlenScientific and Statistical Computing Core, NIMH, NIH, USA.
Gang ChenScientific and Statistical Computing Core, NIMH, NIH, USA.ORCID 0000-0002-2960-089X
Robert W CoxScientific and Statistical Computing Core, NIMH, NIH, USA.
Taylor HanayikWellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, UK.
Chris RordenDepartment of Psychology, University of South Carolina, USA.
Dylan M NielsonMachine Learning Team, NIMH, NIH, USA.
Justin K RajendraScientific and Statistical Computing Core, NIMH, NIH, USA.
Richard C ReynoldsScientific and Statistical Computing Core, NIMH, NIH, USA.

Funding

Scientific and Statistical Computing CoreZICMH002888 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI TAYLOR, PAUL · 2009 to 2025
$30.9M
Machine Learning TeamZICMH002968 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI PEREIRA, FRANCISCO · 2018 to 2025
$13.9M
Extending ezBIDS, NiiVue and dcm2niix for user-friendly cloud-based integration and visualizationRF1MH133701 · NIMH · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI RORDEN, CHRISTOPHER · 2023 to 2023
$2.0M
Intramural NIH HHS ZIC MH002888Intramural NIH HHS ZIC MH002968NIMH NIH HHS RF1 MH133701Wellcome Trust
6 · The paper itself

Abstract

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically under-discussed 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,

Identifiers

PMID38585923
PMCPMC10996659

What OpenQuestion holds

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Registered trials

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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.