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

痴呆症动态静息态功能连接机器学习框架的开发

• 批准号: 10677543
• 负责人: fei jiang
• 金额: $ 14.53万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10677543
• 关键词: Address; Affect; Algorithms; Alzheimer' s Disease; Area; Award; Benchmarking; Biological Markers; Biometry; Brain; Characteristics; Clinical; Cognitive; Data; Data Analyses; Dementia; Development; Doctor of Medicine; Doctor of Philosophy; Electroencephalography; Epidemiology; Foundations; Functional Imaging; Functional Magnetic Resonance Imaging; Goals; Image; Imaging Techniques; Impaired cognition; Individual; Intervention; Knowledge; Learning; Mentors; Methodology; Methods; Monitor; Multimodal Imaging; Nature; Neurodegenerative Disorders; Neurologic Dysfunctions; Neurology; Neurosciences; Pattern; Persons; Procedures; Research; Research Personnel; Rest; Risk Reduction; Sampling; Symptoms; Technical Expertise; Testing; Time; Training; Treatment Efficacy; United States; Validation; Work; biomedical imaging; career; cognitive change; cognitive performance; cognitive testing; early screening; efficacious treatment; feature extraction; feature selection; high dimensionality; imaging biomarker; imaging modality; machine learning framework; machine learning method; machine learning model; method development; multidisciplinary; multimodal neuroimaging; neuroimaging; neuroimaging marker; next generation; predictive modeling; prevent; radiological imaging; statistical and machine learning; success; therapy design; treatment effect

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.

项目总结/文摘

项目成果

Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging.

fMRI 和 MEG 成像中动态静息态功能连接的时变动态网络模型。

• DOI: 10.1016/j.neuroimage.2022.119131
• 发表时间: 2022-07-01
• 期刊: NEUROIMAGE
• 影响因子: 5.7
• 作者: Jiang, Fei;Jin, Huaqing;Gao, Yijing;Xie, Xihe;Cummings, Jennifer;Raj, Ashish;Nagarajan, Srikantan
• 通讯作者: Nagarajan, Srikantan

On high-dimensional Poisson models with measurement error: Hypothesis testing for nonlinear nonconvex optimization.

关于具有测量误差的高维泊松模型：非线性非凸优化的假设检验。

• DOI: 10.1214/22-aos2248
• 发表时间: 2023
• 期刊: Annals of statistics
• 影响因子: 4.5
• 作者: Jiang,Fei;Zhou,Yeqing;Liu,Jianxuan;Ma,Yanyuan
• 通讯作者: Ma,Yanyuan