Pitfalls in the use of voxel-based morphometry as a biomarker: examples from huntington disease

Abstract

Background and purpose: VBM is increasingly used in the study of neurodegeneration, and recently there has been interest in its potential as a biomarker. However, although it is largely "automated," VBM is rarely implemented consistently across studies, and changing user-specified options can alter the results in a way similar to the very biologic differences under investigation.

Materials and methods: This work uses data from patients with HD to demonstrate the effects of several user-specified VBM parameters and analyses: type and level of statistical correction, modulation, smoothing kernel size, adjustment for brain size, subgroup analysis, and software version.

Results: The results demonstrate that changing these options can alter results in a way similar to the biologic differences under investigation.

Conclusions: If VBM is to be useful clinically or considered for use as a biomarker, there is a need for greater recognition of these issues and more uniformity in its application for the method to be both reproducible and valid.

Figures

Fig 1.

Effect of type and level of statistical correction. All SPMs show the same contrast: regions in which the early HD group has reduced GM volume relative to controls (this is true throughout the article unless otherwise stated). SPMs are smoothed at 4-mm FWHM. The 3 SPMs in the top panel show various levels of FWE correction, and the 3 SPMs below show various levels of uncorrected SPMs. The color bar shows the t value and is applicable to all figures in this article.

Fig 2.

Effect of using modulated or unmodulated data. Both SPMs show the same contrast of early HD versus controls, corrected at FWE P < .05, smoothed at 4-mm FWHM.

Fig 3.

Effect of smoothing kernel size. All SPMs show early HD versus controls, corrected at FWE P < .05. The SPMs are smoothed at 4-, 6-, and 8-mm FWHM.

Fig 4.

Graphs demonstrate how TIV and total GM volume vary with age and motor score (an index of HD severity). The top 2 graphs show that the relationship between TIV and both age and motor score is small and not statistically significant. The bottom 2 graphs show that total GM volume decreases with age (r = −0.26, P = .017) and motor score (r = −0.31, P = .0493).

Fig 5.

Effects of adjusting for TIV with and without including total GM volume. All SPMs show early HD versus controls, corrected at FWE P < .05, smoothed at 4-mm FWHM. The top row shows the effect of including or excluding TIV as a covariate. The bottom row shows the effect of adjusting for total GM volume with and without TIV.

Fig 6.

Subgroup analyses. The left SPM shows regions in which a group of high motor scorers have reduced GM volume relative to matched controls. The center SPM shows regions in which a group of low motor scorers have reduced GM volume relative to controls. The right SPM shows the results when the high and low motor scorer groups are compared directly.