The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
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 (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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Cites
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A global optimisation method for robust affine registration of brain images
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Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data
Woolrich, M. W., Ripley, B. D., Brady, M., & Smith, S. M. (2001) · NeuroImage · doi:10.1006/nimg.2001.0931 · Cited by 2,859
Bayesian Density Estimation and Inference Using Mixtures
Escobar, M. D., & West, M. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476550 · Cited by 2,236
Learning the value of information in an uncertain world
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007) · Nature Neuroscience · doi:10.1038/nn1954 · Cited by 2,053
Self-citationMultilevel linear modelling for FMRI group analysis using Bayesian inference
Woolrich, M. W., Behrens, T. E. J., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.12.023 · Cited by 1,719
General multilevel linear modeling for group analysis in FMRI
Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00435-x · Cited by 1,464
Statistical methods of estimation and inference for functional MR image analysis
Bullmore, E. T., Brammer, M. J., Williams, S., Rabe‐Hesketh, S., Janot, N., David, A. S., Mellers, J. D. C., Howard, R. J., & Sham, P. C. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350219 · Cited by 683
Analysis of a large fMRI cohort: statistical and methodological issues for group analyses
Thirion, B., Pinel, P., Mériaux, S., Roche, A., Dehaene, S., & Poline, J.-B. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.054 · Cited by 563
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Cakar, M. E., Cummings, K. K., Bookheimer, S. Y., Dapretto, M., & Green, S. A. (2026) · Autism Research · doi:10.1002/aur.70313
Individual differences in achievement goal orientation predict neural responses to negative feedback
Raizberg, D. A., DePasque, S., Backstrom, V. L., Rivera, L. A., & Tricomi, E. M. (2026) · Trends in Neuroscience and Education · doi:10.1016/j.tine.2026.100303
Metadata
- 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
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