Distributional assumptions in voxel-based morphometry
Abstract
In this paper we address the assumptions about the distribution of errors made by voxel-based morphometry. Voxel-based morphometry (VBM) uses the general linear model to construct parametric statistical tests. In order for these statistics to be valid, a small number of assumptions must hold. A key assumption is that the model's error terms are normally distributed. This is usually ensured through the Central Limit Theorem by smoothing the data. However, there is increasing interest in using minimal smoothing (in order to sensitize the analysis to regional differences at a small spatial scale). The validity of such analyses is investigated. In brief, our results indicate that nonnormality in the error terms can be an issue in VBM. However, in balanced designs, provided the data are smoothed with a 4-mm FWHM kernel, nonnormality is sufficiently attenuated to render the tests valid. Unbalanced designs appear to be less robust to violations of normality: a significant number of false positives arise at a smoothing of 4 and 8 mm when comparing a single subject to a group. This is despite the fact that conventional group comparisons appear to be robust, remaining valid even with no smoothing. The implications of the results for researchers using voxel-based morphometry are discussed.
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Images were smoothed with a 12 mm kernel to make the data normally distributed and reduce false positives.
“The normalized, segmented, and modulated images were then smoothed using a 12 mm kernel to ensure that the data were normally distributed and to limit the number of false positive findings (Salmond et al., 2002).”
From High pregnancy anxiety during mid-gestation is associated with decreased gray matter density in 6-9-year-old children. (2010) · cites this paper as Salmond et al., 2002
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- Authors
- Salmond, C. H., Ashburner, J., Vargha-Khadem, F., Connelly, A., Gadian, D. G., & Friston, K. J.
- Affiliations
- University College London (GB)
- UCL Queen Square Institute of Neurology (GB)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2002-10
- Volume
- 17
- Issue
- 2
- Pages
- 1027-1030
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2113706761
- Cited by
- 256
- References
- 10
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 3 · 2022: 4 · 2021: 4 · 2020: 3 · 2019: 24 · 2018: 9 · 2017: 10 · 2016: 8 · 2015: 14 · 2014: 9 · 2013: 13 · 2012: 12
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust
- Medical Research Council
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- voxel-based morphometry, error distribution, normality assumption, non-normality, data smoothing, general linear model, false positives, unbalanced designs, group comparison, brain structure
- MeSH terms
- Brain Mapping, Child, Data Interpretation, Statistical, False Positive Reactions, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Reference Values, Reproducibility of Results, Linear Models
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