Image based modeling of biomechanical factors for risk assessment of developing periventricular white matter hyperintensities

基于图像的脑室周围白质形成风险评估的生物力学因素建模 物质超强 白质病变，在T2加权磁共振图像上可见白质高信号(WMH)，已被 被证明与认知障碍和痴呆症密切相关。常见于脑室周围 脑区和皮质下白质，WMH在老年人群中普遍存在(60岁)。他们是 在轻度认知障碍和阿尔茨海默病患者中尤为普遍。多发性病理 在WMH中发现了变化，包括脱髓鞘和WM纤维丢失。此外，WMH一直在 与血管缺陷密切相关，俗称“小血管病”。尽管流行和 WMH在认知衰退中的潜在意义，关于WMH在认知衰退中的潜在意义，人们几乎没有深入的了解 脑室周围脑组织的潜在原因和生物物理脆弱性。我们的最终目标是 了解各种生物力学因素在WMH形成中的作用，以预测或干预WMH的形成 WMH进展。这项建议的目的是确定生物机械力(作用于 心搏所致的侧脑室壁)对邻近脑白质的显微结构和形成 脑室周围WMH，通过使用新的MRI方法和基于图像的计算模型。 在这个项目中，我们将开发一个基于3D大脑解剖结构的计算建模框架 结构磁共振图像，通过高速磁共振测量获得的脑室边界变化和 组织的机械性能。基于图像的建模将计算大脑中的压力变化， 尤其是脑室周围白质。高应力的位置将与区域相关联 WMHs、微结构损伤和认知测量。理解生物力学的贡献 影响WMH的因素将允许我们改进WMH的特征，揭示额外的促成因素(例如， 高血压)，并预测认知结果。为了确定生物力学因素在WMH中的作用， 我们提出了以下具体目标： 建议的目标： 特定目标1：开发一种基于磁共振成像的建模方法，用于计算特定受试者的压力 心脏压力脉搏在脑室周围脑组织中的分布。 的特定目标2：确定脉搏压力对脑室周围白质形成的影响。

Image based modeling of biomechanical factors for risk assessment of developing periventricular white matter hyperintensities White matter lesions, visible as white matter hyperintensities (WMH) on T2-weighted MR images, have been shown to have a close association with cognitive impairment and dementia. Commonly found in periventricular brain areas and subcortical white matter, WMH are prevalent in the elderly population (age > 60). They are particularly prevalent in mild cognitive impairment and Alzheimer's disease patients. Multiple pathological changes have been found in WMH, including demyelination and loss of WM fibers. Moreover, WMH has been closely associated with vascular deficits, commonly called “small vessel disease”. Despite the prevalence and potential significance of WMH in cognitive declines, there is little to no in-depth knowledge regarding the underlying causes and biophysical vulnerability of the periventricular brain tissue. Our ultimate goal is to understand the role of various biomechanical factors in formation of WMH in order to predict or intervene in the WMH progression. The objective of this proposal is to determine the effect of biomechanical forces (acting on the lateral ventricular wall due to cardiac pulse) on the adjacent white matter microstructure and formation of periventricular WMH, by using novel MRI methods and image-based computational modeling. In this project, we will develop a computational modeling framework based on 3D brain anatomy obtained with structural MR images, ventricular boundary changes obtained with high-speed MR measurements and mechanical properties of the tissues. The image-based modeling will compute the stress variations in the brain, especially at periventricular white matter. The locations with high stresses will be correlated with the regions of WMHs, microstructural damages and cognitive measures. Understanding the contribution of biomechanical factors to WMH will allow us to improve characterization of WMH, reveal additional contributing factors (e.g., hypertension), and predict cognitive outcomes. In order to determine the role of biomechanical factors in WMH, we propose the following specific aims: Aims proposed:  Specific Aim 1: Develop an MRI-based modeling methodology for computing subject-specific stress distribution in the periventricular brain tissue due to cardiac pressure pulse.  Specific Aim 2: Determine the impact of pulsatile pressure on the formation of periventricular WMH.