Robust group analysis using outlier inference
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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Outliers were de-weighted in the multi-subject statistics using mixture modeling.
“Outliers were de-weighted in the multi-subject statistics using mixture modeling (Woolrich, 2008).”
From The effects of poor quality sleep on brain function and risk taking in adolescence (2013) · cites this paper as Woolrich, 2008
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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)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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