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

项目摘要/摘要生物标志物用于识别临床前或前驱阿尔茨海默氏症疾病的生物标志物的发展非常重要 Terest。尽管已经提出了基于静止fMRI的一些初始结果，但准确性，鲁棒性和可靠性能力仍然相对较低。一个高度紧迫的方向是动态功能活动的发展和功能连接方法。这些方法已被证明是特别有希望的由于大脑的高度动态性和静止fMRI的不受约束性质。目前，有没有可以提供临时，空间和时空动态的完整表征的方法大多数现有的方法是异质亚组或复杂的多尺度关系的特征。我们将开发可以有效捕获动态连接并提供摘要指标的新方法在疾病发作之前，将重点关注对阿尔茨海默氏病的个性化预测。我们提出了一个新颖的模型家族，建立在结构良好的盲目源分离的框架上，以捕获（潜在的非线性）时空动力学的更完整的表征。我们的模型也将支持插入一组丰富的指标，以表征可用的动力学，并与当前的深度比较启用可用型号。我们证明了此类措施可能更敏感和更多的证据准确地分类个体。我们将以各种方式广泛验证我们的方法模拟，同时脑电图/FMRI数据以及对大型正常数据集的评估。我们将应用Devel- 对几个大数据集的操作方法，包括大量的纵向样本在埃默里大学（Emory University）扫描了静止的fMRI，他们还具有CSF淀粉样蛋白和Tau宠物的测量值。我们将使用在这些数据中预测认知能力下降，淀粉样蛋白和TAU水平的开发标记物，并包括发现数据集以及独立的复制数据集。成功完成我们的目标将是重要的第一步朝着提供足够早期开发和评估干预措施的机会对长期预后的积极影响。我们将提供开源工具并在整个过程中发布数据通过Web门户网站和NITRC存储库Github的项目持续时间，因此使其他研究人员能够将自己的方法与我们自己的方法进行比较，并将其应用于各种各样的脑部疾病。我们的工具还广泛应用健康大脑以及许多其他疾病。 37