Sex, function and structure: machine learning the human cerebral vasculature.

大脑的活动依赖于足够的血液流动，通过复杂且难以测量的血管系统提供。由于技术和时间的限制，研究人员通常在一个维度上研究大脑血流；量化成年男性的功能(时间)或结构(空间)。因此，在为数不多的对性别差异进行分层的研究中，很难辨别为什么女性的大脑血流量高于男性，但脑血管疾病(中风、痴呆症、阿尔茨海默病)的发病率存在差异。血管超声(US)测量随着时间的推移生理变化所影响的力。然而，磁共振成像(MRI)提供了高空间分辨率的动脉结构的量化。机器学习可以通过将传统技术中有效的高保真信号相乘来增强脑血管评估，并减少时间限制。尽管有这些技术，但研究脑血管性别差异的研究有限，也没有数据直接比较使用这些方法组合的男性和女性的大脑动脉功能和结构。因此，这项研究旨在开发方法，以加强对人类脑血管系统的测量。这将通过三个战略研究目标实现：目标1：开发、收集和比较使用高时间(US)和空间(MRI)分辨率成像的男性和女性脑血管结构和功能的测量方法：我们的实验室专门使用血管US测量大脑动脉的生理功能，并可以访问指定的MRI研究。这项建议将利用一系列测试来评估脑血管系统的性别差异，使用类内相关性和协变量分析来描述结构和功能指标。目的2：独立和有监督的机器学习方法将用于对人类脑血流调节机制进行分层：机器学习算法将从目标1收集的信号中产生数千种独特的脑血管结果。通过融合算法，独立机器学习将通过结合血压、新陈代谢、呼吸和神经反应来开发脑血管功能的综合指数。我们假设，与传统的一维分析相比，机器学习将增强脑血管评估。目的3：结合脑血管的US和MRI测量方法来量化人类结构和功能之间的关系：本研究的第三个重点是将从MRI图像获得的主要脑血管结构的最先进的三维血管建模与我们在目标2中创建的多因素测量方法相结合。MRI和US测量方法的集成将能够同时量化多个脑血管中的异常血流模式，以及生理刺激期间对脑血管功能的各自风险。

Brain activity is dependent on an adequate blood flow, supplied through a complex and difficult to measure vasculature. Because of technological limitations, and time constraints researchers typically investigate cerebral blood flow in one dimension; quantifying either function (time) or structure (spatial) in adult males.Thus, in the few studies stratifying sex differences, it is difficult to discern why women have a higher cerebral blood flow than men, but have divergent incidence's of cerebrovascular disease (stroke, dementia, Alzheimer's disease). Vascular ultrasound (US) measures forces impacted by physiological changes over time. Whereas, magnetic resonance imaging (MRI) provides high spatial resolution quantification of artery structure. Machine learning can enhance cerebrovascular assessment by multiplying valid high-fidelity signals from traditional techniques and reduce time constraints. Despite these techniques, there is limited research investigating cerebrovascular sex differences, and no data directly comparing cerebral artery function and structure in men and women using a combination of these approaches. Thus, this research aims to develop approaches that enhance the measurement of the cerebral vasculature in humans. This will be achieved through three strategic research aims: Aim 1: Develop, collect, and compare measures of cerebrovascular structure and function in men and women using high temporal (US) and spatial (MRI) resolution imaging: Our lab specializes in measuring physiological function of cerebral arteries, using vascular US, and has access to a research designated MRI. This proposal will utilize a battery of tests to assess sex differences in the cerebral vasculature using intra-class correlation and co variate analysis to describe structure and function metrics. Aim 2: Independent and supervised machine learning approaches will be used to stratify the mechanisms of brain blood flow regulation in humans: Machine learning algorithms will generate thousands of unique cerebrovascular outcomes from signals collected in Aim 1. Independent machine learning, through fusion algorithms, will develop a comprehensive index of cerebrovascular function by incorporating blood pressure, metabolism, respiratory and neural responses. We hypothesize that machine learning will enhance cerebrovascular assessment compared to traditional uni-dimensional analysis. Aim 3: Combine US and MRI measures of the cerebral vasculature to quantify the relationship between structure and function in humans: A tertiary focus of this research is the integration of state-of-the-art 3-dimensional vascular modelling of the major cerebrovascular structures acquired from MRI images, with our multi-factorial measures created in Aim 2. Integration of MRI and US measures will enable the quantification of abnormal flow patterns in multiple cerebrovascular vessels simultaneous, respective risk to cerebrovascular function during physiological stimulation.