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Data driven dynamic activity/connectivity methods for early detection of Alzheimer’s

用于早期检测阿尔茨海默病的数据驱动的动态活动/连接方法

基本信息

批准号：
10289991
负责人：
TULAY ADALI
金额：
$ 77.78万
依托单位：
GEORGIA STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10289991
关键词：
Address Adult Age Aging Algorithms Alzheimer disease detection Alzheimer&apos s Disease Alzheimer’s disease biomarker Amyloid Baltimore Biological Markers Brain Brain Diseases Cognitive Communities Complex Coupling Data Data Discovery Data Set Dementia Detection Development Disease Disease Progression Documentation Early Diagnosis Early Intervention Ensure Evaluation Exhibits Family Frequencies Functional Magnetic Resonance Imaging Future Goals Growth Heterogeneity Impaired cognition Individual Intuition Joints Longitudinal Studies Measures Methods Modeling Nature Onset of illness Pattern Phase Positron-Emission Tomography Prognosis Pythons Research Personnel Rest Sampling Scanning Sleep Source Specificity Structure Study models Subgroup Testing Time Universities Validation Work base biomarker development blind flexibility functional magnetic resonance imaging/electroencephalography improved innovation interest large datasets novel novel marker open source open source tool personalized predictions pre-clinical prodromal Alzheimer&apos s disease repository simulation spatiotemporal tau Proteins tool user-friendly web portal

项目摘要

Project Summary/Abstract The development of biomarkers for identifying preclinical or prodromal Alzheimer’s disorder are of great in- terest. While some initial results based on resting fMRI have been presented, accuracy, robustness, and relia- bility are still relatively low. One highly promising direction is the development of dynamic functional activity and functional connectivity approaches. These approaches have been shown to be especially promising most likely due to the highly dynamic nature of the brain and the unconstrained nature of resting fMRI. Currently, there are no methods that can provide a full characterization of temporal, spatial, and spatio-temporal dynamics nor can most existing approaches characterize heterogenous subgroups or complex multiscale relationships. We will develop new methods that can effectively capture dynamic connectivity and provide summary metrics with a focus on individualized prediction of Alzheimer’s disease well prior to the onset of the illness. We propose a novel family of models that builds on the well-structured framework of joint blind source separation to capture a more complete characterization of (potentially nonlinear) spatio-temporal dynamics. Our models will also pro- duce a rich set of metrics to characterize the available dynamics and enable in depth comparison with currently available models. We show evidence that such measures are likely to be considerably more sensitive and more accurate in classifying individuals. We will extensively validate our approaches in a variety of ways including simulations, concurrent EEG/fMRI data, and evaluation on a large normative data set. We will apply the devel- oped methods to several large datasets including a large longitudinal sample of individuals who have been scanned at Emory University with resting fMRI who also have CSF amyloid and tau PET measures. We will use the developed markers to predict cognitive decline, amyloid, and tau levels in these data and include both a discovery data set as well as an independent replication data set. Successful completion of our aims will be an important first step towards providing an opportunity to develop and evaluate interventions early enough to have a positive impact on long-term prognosis. We will provide open source tools and release data throughout the duration of the project via GitHub, a web portal and the NITRC repository, hence enabling other investigators to compare their own methods with our own as well as to apply them to a large variety of brain disorders. Our tools also have wide application to the study of the healthy brain as well as many other diseases. 37

项目总结/文摘