ADNI Psychometrics: Machine Learning to discern Natural History Aim 2 Supplement

项目摘要/摘要 这是一个资助的R01的补充，名为ADNI心理测量学。本副刊 建立在资助的父母资助的第二个具体目标的基础上。这一目标侧重于 为认知不同的人描述大脑结构和功能- 确定了阿尔茨海默病的亚组。副刊增加了一项技术，用于 分析我们已经在分析的纵向构造数据。新技术 对于结构数据来说，是机器学习。我们有机会与 生物医学信息学方面的有才华的教员，具有特定的专业知识 解剖学数据的机器学习方法(J Gennari)。Genarri博士将监督 机器学习方法来补充我们的各种分析方法 已有针对纵向构造成像的数据 2.纵向成像数据特别重要，因为我们在 不同亚群之间的大脑结构随时间的演变支持了这样的概念 亚群有不同的自然历史，这反过来又在很长一段时间内 挑衅性的结论是，这些亚组的“阿尔茨海默病”代表着不同的 条件。最初并没有设想使用机器学习方法来处理这些数据 提议，但代表了一种特别有价值的补充方法，可以 找出大脑结构进化轨迹的异同 中概述的更传统的分析管道并不明显。 求婚。因此，这非常适合作为行政副刊-这是 通过增加新的专业知识来提高家长研究的价值的机会 以互补和有价值的方式调查一个已经存在的问题 由父母拨款解决。本补充资料建立在相同的基础设施和 Aim 2中提出的问题，但用新奇的内容充实了我们的分析工具 机器学习方法。

Project Summary/Abstract This supplement is to a funded R01 called ADNI Psychometrics. This Supplement builds on the Second Specific Aim of the funded parent grant. That Aim focused on characterizing brain structure and functioning for people with different cognitively- defined subgroups of Alzheimer's disease. The Supplement adds one technique for analyzing the longitudinal structural data we are already analyzing. The new technique for structural data is machine learning. We have the opportunity to collaborate with a talented faculty member in Biomedical Informatics who has specific expertise in machine learning approaches to anatomical data (J Gennari). Dr. Genarri will supervise machine learning approaches to complement the various analytical approaches we already have underway for the longitudinal structural imaging data of Aim 2. Longitudinal imaging data are particularly significant, as any differences we find in the evolution of brain structure over time across subgroups supports the notion that the subgroups have distinct natural histories, which in turn goes a long way towards the provocative conclusion that these subgroups of “Alzheimer's disease” represent distinct conditions. Machine learning approaches to these data were not envisioned in the initial proposal, but represent a particularly valuable complementary approach that may identify similarities and differences in trajectories of the evolution of brain structure that would not be apparent using the more traditional analytic pipelines we outlined in the proposal. This then is the perfect fit for an Administrative Supplement – this is an opportunity to enhance the value of the parent study by adding new expertise to investigate in a complementary and valuable fashion a question that was already addressed by the parent grant. This Supplement builds on the same infrastructure and questions asked in Aim 2, but augments our analytical armamentarium with novel machine learning approaches.