Evidence map›Paper›PMID 32502668›Full record

ArticleNeuroImage2021

Confound modelling in UK Biobank brain imaging.

Fidel Alfaro-Almagro, Paul McCarthy, Soroosh Afyouni, Jesper L R Andersson, Matteo Bastiani, Karla L Miller, Thomas E Nichols, Stephen M Smith

Abstract read
In one paragraph

Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 154 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
154citing papers in PubMed, 6 pooled it
–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

154 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  6. Reproducibility of cerebrovascular reactivity measurements: A systematic review of neuroimaging techniquesJournal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2022
    Pooled it
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  11. The intrinsic cortical geometry of reading.bioRxiv : the preprint server for biology · 2026
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94 more citing papers are in PubMed but not listed here.

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

8 authors.

Fidel Alfaro-AlmagroWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK. Electronic address: fidel.alfaroalmagro@ndcn.ox.ac.uk.
Paul McCarthyWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
Soroosh AfyouniBig Data Institute, University of Oxford, UK.
Jesper L R AnderssonWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
Matteo BastianiWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; NIHR Biomedical Research Centre, University of Nottingham, UK.
Karla L MillerWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
Thomas E NicholsWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Big Data Institute, University of Oxford, UK.
Stephen M SmithWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.

Funding

Department of HealthMedical Research CouncilWellcome Trust 098369Wellcome Trust 098369/Z/12/ZWellcome Trust 203139Wellcome Trust 203139/Z/16/Z
6 · The paper itself

Abstract

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

Indexed as

Biological Specimen BanksBrainNeuroimagingElectronic Data ProcessingHeadHumansTime FactorsUnited KingdomBig data imagingConfoundsData modellingEpidemiological studiesImage analysisMachine learningMulti-modal data integrationStatistica l modelling

Identifiers

PMID32502668
PMCPMC7610719

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.