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Cited by 256Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and Applications

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Distributional Assumptions in Voxel-Based Morphometry

Salmond, C.H., Ashburner, J., Vargha-Khadem, F., Connelly, A., Gadian, D.G., Friston, K.J.·2002·NeuroImage
DOI 10.1006/nimg.2002.1153
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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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Metadata

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)
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2002-10
Volume
17
Issue
2
Pages
1027-1030
ISSN
1053-8119 (ISSN-L)
DOI
10.1006/nimg.2002.1153
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
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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