3D pattern of brain abnormalities in Williams syndrome visualized using tensor-based morphometry
Introduction
Williams syndrome (WS) is a neurodevelopmental microdeletion disorder, resulting in characteristic cardiovascular abnormalities and typical facial features. WS is also an informative model for studying the linkage between genetic deficits and their neurobiological effects on the brain and behavior. The syndrome is associated with deletion of a 1–2 Mb contiguous genomic region containing about 25 genes in chromosome band 7q11.23, including the genes for elastin which has been implicated in congenital heart deficits in persons with WS (Korenberg et al., 2000), such as septal defects or pulmonary/aortic stenosis. Neurobiologically, individuals with WS exhibit mild to moderate mental retardation and learning difficulties, yet there are nonetheless distinctive patterns of relative strengths and weaknesses. Individuals with WS are profoundly impaired in visuoconstructive spatial abilities, impaired in problem solving, but show relative strengths in language, particularly expressive language and face processing. In addition, individuals with WS tend to exhibit a characteristic personality that includes overfriendliness as well as the use of language for social purposes (Bellugi et al., 1999, Bellugi et al., 2001).
Such an imbalance in intellectual ability has been associated with an uneven pattern of relative deficits and excesses in the volume of specific brain regions. Volumetric studies have shown that the volume of the frontal lobes in WS is relatively preserved compared with individuals with Down syndrome or healthy controls (Jernigan et al., 1993, Reiss et al., 2000), but occipital and superior parietal cortices are disproportionally reduced in volume (Reiss et al., 2004, Eckert et al., 2005). Volumetric studies can be labor-intensive, and the spatial detail obtained is limited by the manual delineation of pre-selected regions of interest so it cannot visualize the profile of volume differences at the voxel-by-voxel level. Voxel-based morphometry (VBM) provides more spatial detail regarding the profile of tissue excess and deficits throughout the brain region, by comparing the gray matter density (smoothed gray matter maps) in spatially normalized MR images at each voxel. VBM studies have detected systematic differences in brain morphology between WS and normal subjects, such as reduced volumes in parieto-occipital cortex (Meyer-Lindenberg et al., 2004, Reiss et al., 2004, Boddaert et al., 2006, Eckert et al., 2006a). Nevertheless, these studies have often been based on small samples. Even so, effect sizes in VBM studies are typically small, and may be insufficient to describe morphological variations that may be broadly distributed throughout the entire brain in this neurodevelopmental disorder. More recent work has examined cortical gray matter thickness, using surface-based modeling and elastic warping of sulcal patterns to integrate information across subjects (Thompson et al., 2005). This work identified a circumscribed area of the right perisylvian and inferior temporal regions where the cortex was on average 5–10% thicker in Williams syndrome, suggesting a localized failure of cortical maturation. Other studies have detected temporo-parietal gyrification differences (Eckert et al., 2006b), and increased cortical complexity quantified using fractal dimension measures (Thompson et al., 2005), mean curvature maps (Gaser et al., 2006, Tosun et al., 2006, Tosun et al., 2007), or by counting sulcal branches identified automatically as a graph of connected curves on the cortex (Shi et al., 2007).
In this paper, we apply tensor-based morphometry (TBM), see Davatzikos et al., 1996, Davatzikos et al., 2001, Thompson et al., 2000, Fox et al., 2001, Shen and Davatzikos, 2003, Chung et al., 2004, Studholme et al., 2004, Chiang et al., 2007 for related work, to detect and automatically quantify subtle and distributed patterns of brain volume differences between 41 WS and 39 normal subjects. In the TBM approach, all images are nonlinearly deformed to match a preselected brain image, which acts as a template. Then, the Jacobian determinant (i.e., the local expansion factor) of the deformation fields is used to gauge the local volume differences between the individual images and the template, and these can be analyzed statistically to identify group differences or localized volume increases/reductions at the voxel level. In our implementation of TBM, which we have previously applied to examine brain atrophy in HIV/AIDS (Chiang et al., 2007), nonlinear image deformation is based on a fluid-deformation algorithm by maximizing the Jensen–Rényi divergence (JRD) of the joint intensity histogram (Chiang et al., 2007). Our results demonstrate that TBM may more clearly visualize the unique morphological profile in WS brains, identifying a more extensive but complementary pattern of differences relative to prior work using voxel-based morphometry or volumetric parcellation. Among adult WS subjects, we also examined links between volumes of specific brain regions and intellectual performance. Finally, we examine the distribution of brain volume asymmetry in both WS and control subjects, mapping the profile of group differences.
Section snippets
WS and control subjects
The study analyzed the data of 41 subjects with genetically confirmed Williams syndrome ([mean ± SD]: 29.2 ± 9.2 years of age; range 12–50 years of age; 18M/23F; diagnosis confirmed by fluorescent in situ hybridization tested for deletion of one copy of the elastin gene on chromosome 7) and 39 age-matched healthy controls (27.5 ± 7.4 years of age; range 18–49 years of age; 16M/23F). These subjects were included in Reiss et al. (2004); the same cohort was studied in our prior reports (Thompson et al.,
Target selection and symmetric brain template
Fig. 1 shows brain MR images with lowest and highest TQS, the MDT, and the symmetric template. TQS is a measure of how much each brain deviates from all the others in the sample (including WS and controls), and there was no difference in TQS between WS and control subjects (WS: 2.48 ± 0.19 voxels, ranging from 2.23 to 2.88 voxels; controls: 2.45 ± 0.20 voxels, ranging from 2.12 to 2.97 voxels; P = 0.3 for a group difference). The BIT happened to be one of the control subjects (TQS = 2.12 voxels). The
Discussion
Our study demonstrated that TBM based on fluid registration is helpful to visualize the profile of 3D volumetric differences in WS. Our findings corroborate prior work that used VBM and volumetric assessments (Reiss et al., 2000, Reiss et al., 2004, Schmitt et al., 2001a, Schmitt et al., 2001b, Tomaiuolo et al., 2002, Meyer-Lindenberg et al., 2004, Meyer-Lindenberg et al., 2005b, Boddaert et al., 2006, Eckert et al., 2006a), showing disproportionate reduction in parieto-occipital lobes,
Acknowledgments
This work was funded by grants from the National Institute for Biomedical Imaging and Bioengineering, the National Center for Research Resources, and the National Institute on Aging, (EB01651, RR019771, AG016570 to PT), from the National Institute of Mental Health (K02 MH01142; to ALR), and the National Institute of Child Health and Human Development (R01 HD31715 to ALR and P01 HD33113 to ALR, JRK, DM, AG and UB). Additional support was provided by NCRR Resource grant P41 RR13642 to AWT, and a
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2020, European Journal of Medical GeneticsCitation Excerpt :In people with WS, the thalamus is disproportionately reduced (Reiss et al., 2000; Schmitt et al., 2001; Tomaiuolo et al., 2002; Meyer-Lindenberg et al., 2004). Abnormal gray matter reduction in the thalamus has been linked to their characteristic visuospatial deficits (Chiang et al., 2007; Campbell et al., 2009), but could be expected to contribute as well to their language problems, considering that changes in the thalamus have been associated to key aspects of our cognitive evolution, including the emergence of our species-specific ability to learn and use languages (Boeckx and Benitez-Burraco, 2014). Subjects with WS also show reduced basal ganglia volumes (Jernigan et al., 1993; Bellugi et al., 1999; Reiss et al., 2000; Campbell et al., 2009).