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

项目摘要/摘要 用于识别临床前或先兆阿尔茨海默病的生物标记物的开发具有重要的意义。 很有趣。虽然已有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。 流动性仍然相对较低。一个非常有希望的方向是发展动态功能活动和 功能连接方法。这些方法已被证明是特别有希望的，最有可能 由于大脑的高度动态性质和静息功能磁共振成像的不受限制的性质。目前，有以下几种 没有一种方法可以提供时间、空间和时空动态的完整表征，也不能 大多数现有的方法描述了不同的子群或复杂的多尺度关系。我们会 开发能够有效捕获动态连接并提供摘要指标的新方法 关注阿尔茨海默病发病前的个体化预测。我们提出了一个 建立在结构良好的联合盲源分离框架上的新型模型系列，以捕获 更完整地描述(潜在的非线性)时空动力学。我们的模型还将支持- DUCE有一套丰富的指标来描述可用的动态，并支持与当前的深入比较 可用的型号。我们的证据表明，这样的措施可能会更加敏感和更加 准确地对个体进行分类。我们将通过各种方式广泛验证我们的方法，包括 模拟，并行的EEG/fMRI数据，以及对大型标准数据集的评估。我们将应用这一技术- 对几个大数据集的OpED方法，包括一个大的纵向样本 在埃默里大学进行了静息功能磁共振扫描，他们也进行了脑脊液淀粉样蛋白和tau PET测量。我们将使用 这些数据中用于预测认知功能下降、淀粉样蛋白和tau水平的已开发标记物包括 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一个 向提供机会及早制定和评估干预措施迈出的重要第一步 对长期预后有积极影响。我们将提供开源工具，并在 通过GitHub、Web门户和NITRC存储库的项目持续时间，从而使其他调查人员能够 将他们自己的方法与我们自己的方法进行比较，并将它们应用于各种大脑疾病。我们的工具 对健康大脑以及许多其他疾病的研究也有广泛的应用。 37

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