Computational methods for regional hippocampal morphometry in AD

描述(申请人提供)：PI是一名定量研究人员，具有计算机科学背景，并在图像分析方法、计算机视觉和机器学习领域有出版物记录。他的目标是通过在阿尔茨海默病(AD)和痴呆症的生物医学方面新获得的专业知识来补充他在定量分析方面的优势，将自己确立为阿尔茨海默病(AD)和痴呆症的高效独立研究人员。宾夕法尼亚大学功能神经成像中心主任、国际知名神经学家约翰·德特雷医学教授表示愿意担任主要导师。联合赞助商John Trojanowski教授，医学博士和Murray Grossman教授，医学博士和EDD教授已提出帮助PI获得痴呆症的病理和生理学及其对记忆、语言和思维的影响的专业知识。该研究计划包括开发一个先进的框架，使用体内神经成像来检测和跟踪海马区和海马区旁回的解剖亚区的结构和功能变化。使用这个框架，PI将评估假设，即AD相关的变化在这些颞叶结构的解剖亚区之间是不同的，因此，它们的形态和生理学可以用来早期和准确地预测AD。这些假说将通过挖掘海量的AD患者、AD高危人群和老年对照组的MRI图像数据来验证，这些数据是由NIA/NIH最近发起的ADNI倡议产生的。关联性。随着婴儿潮一代的年龄增长，被AD摧毁的美国家庭数量以及与之相关的社会经济负担预计将大幅增加。虽然目前还没有治愈阿尔茨海默病的方法，但在开发可能阻止或减缓导致阿尔茨海默病的神经退化过程的药物方面，一项相当大的努力正在进行中。为了有效，这些治疗方法需要及早发现。PI的职业目标，由 本申请中提出的研究，旨在提高AD早期诊断的准确性，开发有助于药物开发的非侵入性分析工具，并拓宽我们对AD病理和生理的了解。

DESCRIPTION (provided by applicant): The PI is a quantitative researcher with a background in computer science and a record of publications in the areas of image analysis methodology, computer vision and machine learning. His objective is to establish himself as a highly productive independent researcher of Alzheimer's disease (AD) and dementia by complimenting his strengths in quantitative analysis with a newly acquired expertise in the biomedical aspects of dementia and aging. Prof. John Detre MD, the head of Penn's Center for Functional Neuroimaging and an internationally renowned neurologist, has offered to serve as the principal mentor. The co-sponsors, Prof. John Trojanowski, MD, PhD and Prof. Murray Grossman, MD, EdD have offered to help the PI acquire expertise in the pathology and physiology of dementia as well as its impact on memory, language and thinking. The research plan involves developing an advanced framework for detecting and tracking structural and functional changes in the anatomical subregions of the hippocampus and parahippocampal gyrus using in vivo neuroimaging. Using this framework, the PI will evaluate the hypotheses that AD-related changes differ across the anatomical subregions of these temporal lobe structures and, consequently, that their morphology and physiology can be used to predict AD early and accurately. These hypotheses will be tested by mining the massive database of longitudinal MRI image data of AD patients, people at risk for AD and elderly controls, which is being generated by the ADNI initiative launched recently by the NIA/NIH. RELEVANCE. As the baby boom generation ages, the number of US families devastated by AD and the associated financial burden on the society is expected to increase dramatically. While no cure for AD is known, a sizable effort is underway in developing pharmaceutical agents that may halt or slow down the neurodegenerative processes that cause AD. To be effective, these treatments will require early detection. The Pi's career objectives, addressed by the research proposed in this application, are to improve the accuracy of early AD diagnosis and to develop non-invasive analytic tools that would aid drug development and broaden what we know about the pathology and physiology of AD.