Evidence map›Paper›PMID 35112434›Full record

ArticleHuman brain mapping2022

Restoring statistical validity in group analyses of motion-corrupted MRI data.

Antoine Lutti, Nadège Corbin, John Ashburner, Gabriel Ziegler, Bogdan Draganski, Christophe Phillips, Ferath Kherif, Martina F Callaghan, Giulia Di Domenicantonio

Abstract read
In one paragraph

Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
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  7. Characterizing patterns of diffusion tensor imaging variance in aging brains.medRxiv : the preprint server for health sciences · 2024
    Article
  8. Characterizing patterns of diffusion tensor imaging variance in aging brains.Journal of medical imaging (Bellingham, Wash.) · 2024
    Article
  9. The Neurobiology of Life Course Socioeconomic Conditions and Associated Cognitive Performance in Middle to Late Adulthood.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Article
  10. Article
  11. Article
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  16. Frontiers in neuroscience · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Antoine LuttiLaboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.ORCID 0000-0003-3281-5477
Nadège CorbinCentre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/University Bordeaux, Bordeaux, France.
John AshburnerWellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK.
Gabriel ZieglerInstitute for Cognitive Neurology and Dementia Research, University of Magdeburg, Germany.
Bogdan DraganskiLaboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Christophe PhillipsGIGA Cyclotron Research Centre - in vivo imaging, GIGA Institute, University of Liège, Liège, Belgium.ORCID 0000-0002-4990-425X
Ferath KherifLaboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Martina F CallaghanWellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK.ORCID 0000-0003-0374-1659
Giulia Di DomenicantonioLaboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.

Funding

Wellcome TrustWellcome Trust 203147/Z/16/Z
6 · The paper itself

Abstract

Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterise disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data-driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of quantitative MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.

Indexed as

Magnetic Resonance ImagingHumansMotionQuality ControlSample Sizeheteroscedasticitymotion artefactquality controlquantitative MRIstatistical image analysis

Identifiers

PMID35112434
PMCPMC8933245

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.