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  Restoring statistical validity in group analyses of motion‐corrupted MRI data

Lutti, A., Corbin, N., Ashburner, J., Ziegler, G., Draganski, B., Phillips, C., et al. (2022). Restoring statistical validity in group analyses of motion‐corrupted MRI data. Human Brain Mapping, 43(6), 1973-1983. doi:10.1002/hbm.25767.

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 Creators:
Lutti, Antoine1, Author
Corbin, Nadège2, 3, Author
Ashburner, John3, Author
Ziegler, Gabriel4, Author
Draganski, Bogdan1, 5, Author              
Phillips, Christophe6, Author
Kherif, Ferath1, Author
Callaghan, Martina F.3, Author
Di Domenicantonio, Giulia1, Author
Affiliations:
1Département des Neurosciences Cliniques, Laboratoire de Recherche en Neuroimagerie (LREN), Centre hospitalier universitaire vaudois, Lausanne, Switzerland, ou_persistent22              
2Centre de Résonance Magnétique des Systèmes Biologiques, Bordeaux, France, ou_persistent22              
3Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United Kingdom, ou_persistent22              
4Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University Magdeburg, Germany, ou_persistent22              
5Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
6GIGA Institute, University of Liège, Belgium, ou_persistent22              

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Free keywords: Heteroscedasticity; Motion artefact; Quality control; Quantitative MRI; Statistical image analysis
 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.

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Language(s): eng - English
 Dates: 2022-02-032022-04-15
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1002/hbm.25767
Other: epub 2022
PMID: 35112434
 Degree: -

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Project name : -
Grant ID : 320030_184784, 32003B_135679; 32003B_159780; 324730_192755
Funding program : -
Funding organization : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Project name : -
Grant ID : 203147/Z/16/Z
Funding program : -
Funding organization : Wellcome Trust

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Title: Human Brain Mapping
Source Genre: Journal
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Publ. Info: New York : Wiley-Liss
Pages: - Volume / Issue: 43 (6) Sequence Number: - Start / End Page: 1973 - 1983 Identifier: ISSN: 1065-9471
CoNE: https://pure.mpg.de/cone/journals/resource/954925601686