Abnormal Cortical Complexity and Thickness Profiles Mapped in Williams Syndrome

Harvard University, Cambridge, Massachusetts, United States
The Journal of Neuroscience : The Official Journal of the Society for Neuroscience (Impact Factor: 6.34). 04/2005; 25(16):4146-58. DOI: 10.1523/JNEUROSCI.0165-05.2005
Source: PubMed


We identified and mapped an anatomically localized failure of cortical maturation in Williams syndrome (WS), a genetic condition associated with deletion of approximately 20 contiguous genes on chromosome 7. Detailed three-dimensional (3D) maps of cortical thickness, based on magnetic resonance imaging (MRI) scans of 164 brain hemispheres, identified a delimited zone of right hemisphere perisylvian cortex that was thicker in WS than in matched controls, despite pervasive gray and white matter deficits and reduced total cerebral volumes. 3D cortical surface models were extracted from 82 T1-weighted brain MRI scans (256 x 192 x 124 volumes) of 42 subjects with genetically confirmed WS (mean +/- SD, 29.2 +/- 9.0 years of age; 19 males, 23 females) and 40 age-matched healthy controls (27.5 +/- 7.4 years of age; 16 males, 24 females). A cortical pattern-matching technique used 72 sulcal landmarks traced on each brain as anchors to align cortical thickness maps across subjects, build group average maps, and identify regions with altered cortical thickness in WS. Cortical models were remeshed in frequency space to compute their fractal dimension (surface complexity) for each hemisphere and lobe. Surface complexity was significantly increased in WS (p < 0.0015 and p < 0.0014 for left and right hemispheres, respectively) and correlated with temporoparietal gyrification differences, classified via Steinmetz criteria. In WS, cortical thickness was increased by 5-10% in a circumscribed right hemisphere perisylvian and inferior temporal zone (p < 0.002). Spatially extended cortical regions were identified with increased complexity and thickness; cortical thickness and complexity were also positively correlated in controls (p < 0.03). These findings visualize cortical zones with altered anatomy in WS, which merit additional study with techniques to assess function and connectivity.

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    • "More subtle modifications of gyrification have also been reported, for instance in 22q11.2 deletion syndrome, with gyrification index (Schaer et al., 2006, 2008), combined with fractal indices in Williams syndrome (Gaser et al., 2006; Schmitt et al., 2002; Thompson et al., 2005), or in schizophrenia with sulci shape analysis (Cachia et al., 2008; Plaze et al., 2011). These atypical foldings may present with abnormal cortical thickness. "
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    ABSTRACT: The strong positive-allometric relationship between brain size, cortical extension and gyrification complexity, recently highlighted in the general population, could be modified by brain developmental disorders. Indeed, in case of brain growth insufficiency, the pathophysiological relevance of the “simplified gyral pattern” phenotype is strongly disputed since almost no genotype–phenotype correlations have been found in primary microcephalies. Using surface scaling analysis and newly-developed spectral analysis of gyrification (Spangy), we tested whether the gyral simplification in groups of severe microcephalies related to ASPM, PQBP1 or fetal-alcohol-syndrome could be fully explained by brain size reduction according to the allometric scaling law established in typically-developing control groups, or whether an additional disease effect was to be suspected. We found the surface area reductions to be fully explained by scaling effect, leading to predictable folding intensities measured by gyrification indices. As for folding pattern assessed by spectral analysis, scaling effect also accounted for the majority of the variations, but an additional negative or positive disease effect was found in the case of ASPM and PQBP1-linked microcephalies, respectively. Our results point out the necessity of taking allometric scaling into account when studying the gyrification variability in pathological conditions. They also show that the quantitative analysis of gyrification complexity through spectral analysis can enable distinguishing between even (predictable, non-specific) and uneven (unpredictable, maybe disease-specific) gyral simplifications.
    NeuroImage 11/2014; 102:317–331. DOI:10.1016/j.neuroimage.2014.07.057 · 6.36 Impact Factor
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    • "Thus, amusia appears to be a disorder of disconnectivity rather than strictly auditory perception. Intriguingly, WS is associated with marked reductions in overall white matter, including in the frontal lobe (Reiss et al., 2000; Thompson et al., 2005), as well as abnormal directionality of white matter tracts (Marenco et al., 2007). Thus, despite their auditory sensitivities – and relatively preserved auditory cortices (Reiss et al., 2004; Martens et al., 2010) – rates of amusia may actually be higher in individuals with WS than in the TD population. "
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    ABSTRACT: Williams syndrome (WS), a genetic, neurodevelopmental disorder, is of keen interest to music cognition researchers because of its characteristic auditory sensitivities and emotional responsiveness to music. However, actual musical perception and production abilities are more variable. We examined musicality in WS through the lens of amusia and explored how their musical perception abilities related to their auditory sensitivities, musical production skills, and emotional responsiveness to music. In our sample of 73 adolescents and adults with WS, 11% met criteria for amusia, which is higher than the 4% prevalence rate reported in the typically developing (TD) population. Amusia was not related to auditory sensitivities but was related to musical training. Performance on the amusia measure strongly predicted musical skill but not emotional responsiveness to music, which was better predicted by general auditory sensitivities. This study represents the first time amusia has been examined in a population with a known neurodevelopmental genetic disorder with a range of cognitive abilities. Results have implications for the relationships across different levels of auditory processing, musical skill development, and emotional responsiveness to music, as well as the understanding of gene-brain-behavior relationships in individuals with WS and TD individuals with and without amusia.
    Frontiers in Psychology 08/2013; 4:525. DOI:10.3389/fpsyg.2013.00525 · 2.80 Impact Factor
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    • "sensitivity (59.52%), positive predictive value (100.00%) and negative predictive value (70.18%) were achieved when we used mTBM features from the right cortical hemisphere for the training and testing. Consistent with some data from prior WS studies (Thompson et al., 2005), the right half of the brain may contain more diagnostically useful information relevant to WS classification. This assumption is supported by our classification results. "
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    ABSTRACT: Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification.
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