Quantitative Methods for Neuroimaging Studies of Interventions in Aging

描述(由申请人提供)：NIH导师量化研究职业生涯奖的提案请求支持王永梅博士开始专注于成像研究的教职生涯，该研究旨在研究纵向干预对老年人大脑结构和功能的影响及其与认知和表现的关系。该申请提出了在神经成像、桥梁工程、统计学和神经科学领域的研究职业发展计划。该计划包括两个重叠的阶段：1)教学阶段，强调培训，包括认知神经科学、成像、衰老和干预领域的课程作业和实验室工作，以补充王博士在电气工程方面的博士培训和现有的统计学经验；2)发展阶段，重点是拟议研究的密集发展。这两个阶段将由导师和顾问在认知神经科学、大脑可塑性、生物医学成像、衰老和干预领域密切监督。神经成像技术，如磁共振成像(MRI)和功能磁共振成像(FMRI)，已被证明是表征和了解人脑结构和功能的强大工具。然而，由于现有方法的局限性，仍然需要稳健和有效的统计图像分析方法。开发这些分析技术时必须充分了解所使用的神经成像方法和相关的认知神经科学，这一点至关重要。我们建议开发、实现和验证用于脑MRI和fMRI图像的可靠和敏感分析的集成计算算法，具体目标如下：1)开发、验证和结合新的、高效的单变量和多变量形态计量分析方法。2)开发和评估综合功能血流动力学反应和连通性研究方法。3)将这些方法应用于从导师单独资助的NIA项目中收集的MRI和fMRI数据，以检验有氧健身训练对老年人大脑结构和功能的影响；将被测试的神经科学假说是：在为期一年的干预过程中，有氧健身的改善将导致：i)人脑灰质和白质体积的增加和皮质下结构的形状变化；以及ii)潜在神经回路的变化。4)开发了实现上述方法的脑图像分析工具箱。

DESCRIPTION (provided by applicant): This proposal for an NIH Mentored Quantitative Research Career Award requests support for Dr. Yongmei Michelle Wang as she embarks on a faculty career focused on imaging studies which examine the influence of longitudinal interventions on brain structure and function, and its relationship to cognition and performance, in older adults. The application proposes a research career development plan in the field of neuroimaging, bridging engineering, statistics, and neuroscience. The plan includes two overlapping phases: 1) a didactic phase that emphasizes training, including coursework and laboratory work in the area of cognitive neuroscience, imaging, aging, and interventions to complement Dr. Wang's doctoral training in Electrical Engineering and existing experience in Statistics; and 2) a development phase that focuses on intense development of the proposed research. These two phases will be closely supervised by the mentor and advisor in the area of cognitive neuroscience, brain plasticity, biomedical imaging, aging and interventions. Neuroimaging techniques, such as magnetic resonance imaging (MRI) and functional MRI (fMRI), have been shown to be powerful for characterizing and understanding the structure and function of the human brain. There remains a need, however, for robust and efficient statistical image analysis methods due to the limitations of existing approaches. It is crucial that these analysis techniques be developed with a full understanding of the neuroimaging methods used and the relevant cognitive neuroscience. We propose to develop, implement, and validate integrated computational algorithms for reliable and sensitive analysis of brain MRI and fMRI images, with the following specific aims: 1) Develop, validate and combine novel and efficient univariate and multivariate morphometry analysis methods. 2) Develop and evaluate integrated functional hemodynamic response and connectivity study approaches. 3) Apply these methods to the MRI and fMRI data being collected from separately funded NIA project of the mentor, to examine the effects of aerobic fitness training on brain structure and function of older adults; the neuroscience hypothesis to be tested are: improvements in aerobic fitness, over the course of a 1 year intervention, will result in i) increases in gray and white matter volume and shape changes of subcortical structures of the human brain; and ii) changes in the underlying neural circuits. 4) Develop a brain image analysis toolbox implementing the above methods.