Empowering Diffusion MRI Measures by Integrating White and Grey Matter Morphology

描述（由申请人提供）：我们将开发新的几何算法，并将其应用于从阿尔茨海默病神经成像倡议（ADNI）和澳大利亚双胞胎数据集获得的脑扩散MRI图像。尽管在灰质的脑成像测量方面取得了重大进展，但仍然缺乏通过整合脑白色物质和灰质形态的有效脑多模态成像算法。在目前的计画中，我们将发展新的扩散磁振造影分析方法，使用拟共形几何与体积谐波图。具体而言，我们将开发1）用于纤维束成像和网络分析的多模态脑表面分割，以及2）用于测量白色物质形态的基于脑白色物质多元张量的形态测量。拟议的系统将专注于开发一个连贯的计算框架，以整合白色和灰质形态，并计算其完整的几何特征，以捕捉微妙的大脑白色物质的变化。这可能提供了一种有效的方法来确定独特的大脑网络结构或次区域区域，用于诊断组差异比较，并确定可能导致大量人群中某些结构变异的遗传效应。调查我们的可靠性和实用性 方法，我们寻求1）构建结构脑网络与tractography多模态表面parcellation结果，和2）计算脑白色物质的多元张量为基础的形态计量学（WM-mTBM）功能。计算的连通性网络测量和WM-mTBM特征将通过1）ADNI数据集中的诊断组间比较和2）澳大利亚双胞胎数据集中的遗传影响分析进行验证。

DESCRIPTION (provided by applicant): We will develop and apply novel geometric algorithms to brain diffusion MRI images obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian twin datasets. Despite major advances in brain imaging measures on grey matter, there is still a lack of efficient brain multimodal imaging algorithms by integrating brain white matter and grey matter morphology. In the current project, we will develop novel diffusion MRI analysis methods using quasiconformal geometry and volumetric harmonic map. Specifically, we will develop 1) multimodal brain surface parcellation for tractography and network analysis, and 2) brain white matter multivariate tensor-based morphometry to measure white matter morphometry. The proposed system will focus on developing a coherent computational framework to integrate both white and grey matter morphology and compute their complete geometric features to capture subtle brain white matter changes. This may provide an effective way to pinpoint unique brain network structure or subregional areas for diagnostic group difference comparison and identify genetic effects that might account for some of the structural variations in a large population. To investigate the reliability and practicality of our method, we seek to 1) construct structural brain network with tractography on multimodal surface parcellation results, and 2) compute brain white matter multivariate tensor-based morphometry (WM-mTBM) features. The computed connectivity network measures and the WM-mTBM features will be validated by 1) between-diagnostic-group comparisons in the ADNI dataset, and 2) genetic influence analysis in the Australian twin dataset.