Impact of scale space search on age- and gender-related changes in MRI-based cortical morphometry.
ABSTRACT In magnetic resonance imaging based brain morphometry, Gaussian smoothing is often applied to increase the signal-to-noise ratio and to increase the detection power of statistical parametric maps. However, most existing studies used a single smoothing filter without adequately justifying their choices. In this article, we want to determine the extent for which performing a morphometry analysis using multiple smoothing filters, namely conducting a scale space search, improves or decreases the detection power. We first compared scale space search with single-filter analysis through a simulated population study. The multiple comparisons in our four-dimensional scale space searches were corrected for using a unified P-value approach. Our results illustrate that, compared with a single-filter analysis, a scale space search analysis can properly capture the variations in analysis results caused by variations in smoothing, and more importantly, it can obviously increase the sensitivity for detecting brain morphometric changes. We also show that the cost of an increased critical threshold for conducting a scale space search is very small. In the second experiment, we investigated age and gender effects on cortical volume, thickness, and surface area in 104 normal subjects using scale space search. The obtained results provide a perspective of scale space theory on the morphological changes with age and gender. These results suggest that, in exploratory studies of aging, gender, and disease, conducting a scale space search is essential, if we are to produce a complete description of the structural changes or abnormalities associated with these dimensions. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.
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ABSTRACT: Idiopathic rapid eye movement sleep behavior disorder is a parasomnia that is a risk factor for dementia with Lewy bodies and Parkinson's disease. Brain function impairments have been identified in this disorder, mainly in the frontal and posterior cortical regions. However, the anatomical support for these dysfunctions remains poorly understood. We investigated gray matter thickness, gray matter volume, and white matter integrity in patients with idiopathic rapid eye movement sleep behavior disorder. Twenty-four patients with polysomnography-confirmed idiopathic rapid eye movement sleep behavior disorder and 42 healthy individuals underwent a 3-tesla structural and diffusion magnetic resonance imaging examination using corticometry, voxel-based morphometry, and diffusion tensor imaging. In the patients with idiopathic rapid eye movement sleep behavior disorder, decreased cortical thickness was observed in the frontal cortex, the lingual gyrus, and the fusiform gyrus. Gray matter volume was reduced in the superior frontal sulcus only. Patients showed no increased gray matter thickness or volume. Diffusion tensor imaging analyses revealed no significant white matter differences between groups. Using corticometry in patients with idiopathic rapid eye movement sleep behavior disorder, several new cortical regions with gray matter alterations were identified, similar to those reported in dementia with Lewy bodies and Parkinson's disease. These findings provide some anatomical support for previously identified brain function impairments in this disorder. © 2014 International Parkinson and Movement Disorder Society.Movement Disorders 02/2014; · 5.63 Impact Factor
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ABSTRACT: The aim of this paper is to develop a spatial Gaussian predictive process (SGPP) framework for accurately predicting neuroimaging data by using a set of covariates of interest, such as age and diagnostic status, and an existing neuroimaging data set. To achieve better prediction, we not only delineate spatial association between neuroimaging data and covariates, but also explicitly model spatial dependence in neuroimaging data. The SGPP model uses a functional principal component model to capture medium-to-long-range (or global) spatial dependence, while SGPP uses a multivariate simultaneous autoregressive model to capture short-range (or local) spatial dependence as well as cross-correlations of different imaging modalities. We propose a three-stage estimation procedure to simultaneously estimate varying regression coefficients across voxels and the global and local spatial dependence structures. Furthermore, we develop a predictive method to use the spatial correlations as well as the cross-correlations by employing a cokriging technique, which can be useful for the imputation of missing imaging data. Simulation studies and real data analysis are used to evaluate the prediction accuracy of SGPP and show that SGPP significantly outperforms several competing methods, such as voxel-wise linear model, in prediction. Although we focus on the morphometric variation of lateral ventricle surfaces in a clinical study of neurodevelopment, it is expected that SGPP is applicable to other imaging modalities and features.NeuroImage 11/2013; · 6.25 Impact Factor
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ABSTRACT: Many large-scale longitudinal imaging studies have been or are being widely conducted to better understand the progress of neuropsychiatric and neurodegenerative disorders and normal brain development. The goal of this article is to develop a multiscale adaptive generalized estimation equation (MAGEE) method for spatial and adaptive analysis of neuroimaging data from longitudinal studies. MAGEE is applicable to making statistical inference on regression coefficients in both balanced and unbalanced longitudinal designs and even twin and familial studies, whereas standard software platforms have several major limitations in handling these complex studies. Specifically, conventional voxel-based analyses in these software platforms involve Gaussian smoothing imaging data and then independently fitting a statistical model at each voxel. However, the conventional smoothing methods suffer from the lack of spatial adaptivity to the shape and spatial extent of region of interest and the arbitrary choice of smoothing extent, while independently fitting statistical models across voxels does not account for the spatial properties of imaging observations and noise distribution. To address such drawbacks, we adapt a powerful propagation-separation (PS) procedure to sequentially incorporate the neighboring information of each voxel and develop a new novel strategy to solely update a set of parameters of interest, while fixing other nuisance parameters at their initial estimators. Simulation studies and real data analysis show that MAGEE significantly outperforms voxel-based analysis.NeuroImage 01/2013; · 6.25 Impact Factor