Cited by 504Statistical Methods and Bayesian InferenceFunctional Brain Connectivity StudiesAdvanced Statistical Methods and Models

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Robust group analysis using outlier inference

Woolrich, Mark·2008·NeuroImage

DOI 10.1016/j.neuroimage.2008.02.042

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Abstract

Neuroimaging group studies are typically performed with the assumption that subjects used are randomly drawn from a population of subjects. The population of subjects is assumed to have a distribution of effect sizes associated with it that are Gaussian distributed. However, in practice, group studies can include "outlier" subjects whose effect sizes are completely at odds with the general population for reasons that are not of experimental interest. If ignored, these outliers can dramatically affect the inference results. To solve this problem, we propose a group inference approach which includes inference of outliers using a robust general linear model (GLM) approach. This approach models the errors as being a mixture of two Gaussian distributions, one for the normal population and one for the outliers. Crucially the robust GLM is part of a traditional hierarchical group model which uses GLMs at each level of the hierarchy. This combines the benefits of outlier inference with the benefits of using variance information from lower levels in the hierarchy. A Bayesian inference framework is used to infer on the robust GLM, while using the lower level variance information. The performance of the method is demonstrated on simulated and fMRI data and is compared with iterative reweighted least squares and permutation testing.

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Authors: Woolrich, M.
Affiliations: John Radcliffe Hospital (GB)
Corresponding author: Woolrich, Mark
ORCID: Woolrich, Mark
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2008-06
Volume: 41
Issue: 2
Pages: 286-301
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2008.02.042
OpenAlex: W2052515792
Cited by: 504
References: 20
Citations by year: 2026: 13 · 2025: 16 · 2024: 17 · 2023: 27 · 2022: 20 · 2021: 20 · 2020: 39 · 2019: 31 · 2018: 41 · 2017: 43 · 2016: 41 · 2015: 33 · 2014: 32 · 2013: 30 · 2012: 37
FWCI: 19.93
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Funders: Engineering and Physical Sciences Research Council (EP/C539788/1)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Keywords: Gaussian mixture model, Bayesian inference, effect size distribution, iteratively reweighted least squares, permutation tests
MeSH terms: Algorithms, Brain, Humans, Magnetic Resonance Imaging, Models, Statistical, Normal Distribution

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