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Innovative MRI Techniques in Neuroimaging Approaches for Cerebrovascular Diseases and Vascular Cognitive Impairment

机译:神经影像学方法在脑血管疾病和血管性认知障碍中的创新MRI技术

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摘要

Cognitive impairment and dementia are recognized as major threats to public health. Many studies have shown the important role played by challenges to the cerebral vasculature and the neurovascular unit. To investigate the structural and functional characteristics of the brain, MRI has proven an invaluable tool for visualizing the internal organs of patients and analyzing the parameters related to neuronal activation and blood flow in vivo. Different strategies of imaging can be combined to obtain various parameters: (i) measures of cortical and subcortical structures (cortical thickness, subcortical structures volume); (ii) evaluation of microstructural characteristics of the white matter (fractional anisotropy, mean diffusivity); (iii) neuronal activation and synchronicity to identify functional networks across different regions (functional connectivity between specific regions, graph measures of specific nodes); and (iv) structure of the cerebral vasculature and its efficacy in irrorating the brain (main vessel diameter, cerebral perfusion). The high amount of data obtainable from multi-modal sources calls for methods of advanced analysis, like machine-learning algorithms that allow the discrimination of the most informative features, to comprehensively characterize the cerebrovascular network into specific and sensitive biomarkers. By using the same techniques of human imaging in pre-clinical research, we can also investigate the mechanisms underlying the pathophysiological alterations identified in patients by imaging, with the chance of looking for molecular mechanisms to recover the pathology or hamper its progression.
机译:认知障碍和痴呆被认为是对公共健康的主要威胁。许多研究表明,挑战对脑血管和神经血管单位发挥了重要作用。为了研究大脑的结构和功能特征,MRI已证明是一种非常有用的工具,可用于可视化患者的内部器官并分析与体内神经元激活和血流有关的参数。可以组合不同的成像策略以获得各种参数:(i)皮质和皮质下结构的测量(皮质厚度,皮质下结构的体积); (ii)评估白质的微观结构特征(分数各向异性,平均扩散率); (iii)神经元的激活和同步性,以识别跨不同区域的功能网络(特定区域之间的功能连接性,特定节点的图形度量); (iv)脑脉管系统的结构及其对大脑的刺激作用(主血管直径,脑灌注)。从多模式来源获得的大量数据要求进行高级分析的方法,例如机器学习算法,该方法可以区分最具信息性的特征,从而将脑血管网络全面表征为特异性和敏感的生物标记。通过在临床前研究中使用相同的人类成像技术,我们还可以研究通过成像确定的患者病理生理变化的潜在机制,并有机会寻找恢复病理或阻碍其进展的分子机制。

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