Open AccessRestoring statistical validity in group analyses of motion‐corrupted MRI data
Author(s) -
Lutti Antoine,
Corbin Nadège,
Ashburner John,
Ziegler Gabriel,
Draganski Bogdan,
Phillips Christophe,
Kherif Ferath,
Callaghan Martina F.,
Di Domenicantonio Giulia
Publication year - 2022
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25767
Subject(s) - image quality , artificial intelligence , measure (data warehouse) , computer science , image (mathematics) , motion (physics) , magnetic resonance imaging , pattern recognition (psychology) , quality (philosophy) , computer vision , statistical power , statistics , mathematics , data mining , medicine , physics , quantum mechanics , radiology
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.