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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

项目概要/摘要用于识别临床前或前驱阿尔茨海默病的生物标志物的开发具有重要意义。兴趣。虽然已经提出了一些基于静息功能磁共振成像的初步结果，但准确性、稳健性和可靠性能力还是比较低的。一个非常有前途的方向是动态功能活动的发展和功能连接方法。这些方法已被证明特别有前途由于大脑的高度动态特性和静息功能磁共振成像的不受约束的特性。目前，有没有任何方法可以提供时间、空间和时空动力学的完整表征，也不能大多数现有方法都描述异质子组或复杂的多尺度关系。我们将开发新方法，可以有效捕获动态连接并提供摘要指标重点关注阿尔茨海默病发病前的个体化预测。我们提出一个新颖的模型系列，建立在联合盲源分离的结构良好的框架之上，以捕获（潜在非线性）时空动力学的更完整的表征。我们的模型也将亲产生一组丰富的指标来描述可用动态并能够与当前进行深入比较可用型号。我们提供的证据表明，此类措施可能更加敏感、更加有效。准确地对个体进行分类。我们将通过多种方式广泛验证我们的方法，包括模拟、并发脑电图/功能磁共振成像数据以及对大型规范数据集的评估。我们将应用该开发对几个大型数据集进行了操作方法，其中包括已被研究过的个体的大型纵向样本在埃默里大学进行静息功能磁共振成像扫描，同时进行脑脊液淀粉样蛋白和 tau PET 测量。我们将使用开发的标记物可预测这些数据中的认知衰退、淀粉样蛋白和 tau 蛋白水平，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是提供尽早制定和评估干预措施的机会，以便对长期预后产生积极影响。我们将全程提供开源工具并发布数据通过 GitHub、门户网站和 NITRC 存储库了解项目的持续时间，从而使其他研究人员能够将他们自己的方法与我们自己的方法进行比较，并将其应用于多种脑部疾病。我们的工具也广泛应用于健康大脑以及许多其他疾病的研究。 37