Moderated t-tests for group-level fMRI analysis

•We introduce a moderated t-statistic for performing group-level fMRI analysis.•The approach helps alleviate problems related to small sample sizes.•The approach outperforms several standard approaches.•An R-package is introduced for application of the method to fMRI data. In recent years, there has...

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Vydáno v:NeuroImage (Orlando, Fla.) Ročník 237; s. 118141
Hlavní autoři: Wang, Guoqing, Muschelli, John, Lindquist, Martin A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Inc 15.08.2021
Elsevier Limited
Elsevier
Témata:
Adult
Connectome
Data Interpretation, Statistical
Estimates
fMRI
Functional magnetic resonance imaging
Functional Neuroimaging - methods
Genomics
Group analysis
Humans
Hypotheses
LIMMA
LIMMI
Linear Models
Magnetic Resonance Imaging - methods
Models, Statistical
Moderated t-test
Performance evaluation
Psychomotor Performance
Statistical analysis
Student's t-test
fMRI
Group analysis
Moderated t-test
LIMMA
LIMMI
ISSN:1053-8119, 1095-9572, 1095-9572
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Shrnutí:•We introduce a moderated t-statistic for performing group-level fMRI analysis.•The approach helps alleviate problems related to small sample sizes.•The approach outperforms several standard approaches.•An R-package is introduced for application of the method to fMRI data. In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that such studies have low statistical power, as well as reduced likelihood for statistically significant results to be true effects. The prevalence of these studies has led to a situation where a large number of published results are not replicable and likely false. Despite this growing body of evidence, small sample fMRI studies continue to be regularly performed; likely due to the high cost of scanning. In this report we investigate the use of a moderated t-statistic for performing group-level fMRI analysis to help alleviate problems related to small sample sizes. The proposed approach, implemented in the popular R-package LIMMA (linear models for microarray data), has found wide usage in the genomics literature for dealing with similar issues. Utilizing task-based fMRI data from the Human Connectome Project (HCP), we compare the performance of the moderated t-statistic with the standard t-statistic, as well as the pseudo t-statistic commonly used in non-parametric fMRI analysis. We find that the moderated t-test significantly outperforms both alternative approaches for studies with sample sizes less than 40 subjects. Further, we find that the results were consistent both when using voxel-based and cluster-based thresholding. We also introduce an R-package, LIMMI (linear models for medical images), that provides a quick and convenient way to apply the method to fMRI data.
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Credit authorship contribution statement
Guoqing Wang: Conceptualization, Methodology, Software, Formal analysis, Writing - original draft. John Muschelli: Formal analysis, Software, Writing - review & editing. Martin A. Lindquist: Conceptualization, Methodology, Writing - review & editing, Supervision, Funding acquisition.
ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2021.118141