Development of Dynamic Resting State Functional Connectivity Machine Learning Framework for Dementia

项目摘要/摘要 这项建议的目标是为具有以下资格的候选人费江博士提供一个强有力的培训课程 在统计和机器学习研究方面有良好的基础，使她成为一名独立的 fi定量数据分析和统计/机器学习方法开发领域的研究员 神经影像研究。提出的研究旨在提取动态静息状态功能连通性 并将其用于预测认知功能衰退。中心假设是 休眠状态的功能连通性在成像采集期间改变，并且该动态模式是 对认知衰退的最佳预测至关重要。为了证明这一假设，一种独特的机器学习-- 提出了ING框架，用于(1)稳健地从多通道中提取动态静止态功能连通性 成像；(2)识别与个体认知分数相关的重要特征；以及(3)预测 认知功能减退使用Identifi的重要特征。成功完成拟议的研究将 为动态静息状态的提取和分析提供新一代机器学习框架 功能连通性和通向可用于评估治疗效果的潜在终点。 认识到拟议工作的多学科性质，将指导提交人并与其密切合作 来自与该项目相关的多个科学领域(神经成像、神经退行性变)的专家委员会 疾病，生物统计学)：Srikantan Nagarajan(主要导师)，放射学和生物医学系博士 影像，Ashish Raj(共同导师)，博士，放射和生物医学成像系，William W.Seeley(ad- 约翰·科尔纳克(顾问)，流行病学系 首页--期刊主要分类--期刊细介绍--期刊题录与文摘--期刊详细文摘内容 (合作者)，博士，流行病学和生物统计学系。该委员会将由Dr。 那加拉扬。目标是到K25结束时，江博士将拥有必要的知识、技术技能， 和专业知识提交一份成功的R01提案，整合她在统计和机器学习方面的专业知识- 与神经科学成像有关的问题和方法知识的ING方法，获得 在这段训练期间。

Project Summary/Abstract The objective of this proposal is to provide a robust course of training for Fei Jiang, Ph.D., a candidate with an excellent foundation in statistical and machine learning research, to enable her to become an independent investigator in the field of quantitative data analysis and statistical/machine learning methods development for neuroimaging research. The proposed research aims to extract dynamic resting-state functional connectivity from multimodality imaging and use them for the prediction of cognitive decline. The central hypothesis is that the resting state functional connectivity changes over the imaging acquisition period, and this dynamic pattern is crucial for the optimal prediction of cognitive decline. Towards proving this hypothesis, a unique machine learn- ing framework is proposed to (1) robustly extract dynamic resting-state functional connectivity from multimodality imaging; (2) identify the important features that are associated with individuals' cognitive scores; and (3) predict cognitive decline using the identified important features. Successful completion of the proposed research will provide the next generation machine learning framework for the extraction and analysis of dynamic resting-state functional connectivity and lead to potential endpoints that can be used in the assessment of treatment effects. Recognizing the multidisciplinary nature of the work proposed, the author will be mentored and work closely with an expert committee from multiple scientific areas of relevance to the project (Neuroimaging, Neurodegenerative disease, Biostatistics): Srikantan Nagarajan (primary mentor), Ph.D., Department of Radiology and Biomedical Imaging, Ashish Raj (co-mentor), Ph.D., Department Radiology and Biomedical Imaging, William W. Seeley (ad- visor), M.D., Ph.D., Department of Neurology, John Kornak (advisor), Ph.D., Department of Epidemiology and Biostatistics, Marilu Gorno Tempini (collaborator), M.D., Ph.D., Department of Neurology, Charles McCulloch (collaborator), Ph.D., Department of Epidemiology and Biostatistics. This committee will be coordinated by Dr. Nagarajan. The goal is that by the end of the K25, Dr. Jiang will have the requisite knowledge, technical skills, and expertise to submit a successful R01 proposal that integrates her expertise in statistical and machine learn- ing methods with a knowledge of the questions and approaches pertaining to imaging in neuroscience, acquired through this training period.