Voxel-based morphometry and automated lobar volumetry: The trade-off between spatial scale and statistical correction

Abstract

Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller subregion of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobarscale ROI volumetry for assessing gray matter volume differences between groups.

Grey matter volume differences associated with gender in children with attention-deficit/hyperactivity disorder: A voxel-based morphometry study

“…Indeed, ROI methods yield a single value for the volume of the region examined, obtained after averaging signal over the ROI. This signal averaging can cause a dilution of the measure of the volume difference, especially when this difference is only present in a limited part of the ROI (Voormolen et al, 2010). For this reason, VBM has been shown to outperform ROI methods when detecting focal differences in morphology (Voormolen et al, 2010).…”

“…This signal averaging can cause a dilution of the measure of the volume difference, especially when this difference is only present in a limited part of the ROI (Voormolen et al, 2010). For this reason, VBM has been shown to outperform ROI methods when detecting focal differences in morphology (Voormolen et al, 2010). However, theoretically, ROI methods remain superior when between-group differences are distributed uniformly over a small ROI, since the ROI analysis at this spatial scale benefits from substantial signal averaging (Voormolen et al, 2010).…”

“…For this reason, VBM has been shown to outperform ROI methods when detecting focal differences in morphology (Voormolen et al, 2010). However, theoretically, ROI methods remain superior when between-group differences are distributed uniformly over a small ROI, since the ROI analysis at this spatial scale benefits from substantial signal averaging (Voormolen et al, 2010). Both methods can therefore provide different types of information and are considered as complementary (Giuliani et al, 2005).…”

Association of Schizophrenia in 22q11.2 Deletion Syndrome and Gray Matter Volumetric Deficits in the Superior Temporal Gyrus

“…Image analysis-The image processing steps have been described in detail elsewhere (24,25). In brief, whole-brain images were analyzed by the voxel-based morphometry method using SPM5 (Wellcome Trust Centre for Neuroimaging, London) under MATLAB (MathWorks, Natick, Mass.)…”

Cortical thickness is linked to executive functioning in adulthood and aging

“…Specifically, our goal was twofold: (a) to better characterize the relations between cortical thickness of brain regions associated with the WCST (as defined in previous functional neuroimaging studies) [Buschbaum et al, 2005;Nyhus and Barcelo, 2009] To accomplish these goals, in a sample of 73 younger and 56 older healthy adults, we (a) measured cortical thickness, a clearly defined anatomical property (distance between the gray/white matter boundary and the cortical surface) [Fischl and Dale, 2000;MacDonald et al, 2000] that better reflects the surface-based organization of the cortex than gray-matter density or gyral/lobar volume [Hutton et al, 2009;Im et al, 2006a,b]; (b) used higher spatial precision of the measurement by calculating cortical thickness at each location of the cortical mantle [Fischl and Dale, 2000], using procedures that have been validated against histological [Rosas et al, 2002] and manual [Kuperberg et al, 2003;Salat et al, 2004] measurements; (c) applied a hypothesis-free search of the entire cerebrum, known to be more sensitive to focal effects than averaging a brain measure over a large region [e.g., Barrick et al, 2010;Voormolen et al, 2010;Ziegler et al, 2010]; and (d) used a single standardized test, WCST, which allows direct comparison to previous functional neuroimaging studies [Buchsbaum et al, 2005;Nyhus and Barcelo, 2009]. The study was guided by two main hypotheses.…”