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

项目总结/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发展具有重要意义- 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，鲁棒性和可靠性仍然存在。 能力仍然相对较低。一个非常有前途的方向是开发动态功能活性， 功能连接方法。这些方法已被证明是特别有前途的最有可能 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 没有一种方法可以提供时间、空间和时空动态的完整表征， 大多数现有方法的特征在于异质子群或复杂的多尺度关系。我们将 开发新的方法，可以有效地捕捉动态连接，并提供摘要指标， 专注于阿尔茨海默病发病前的个体化预测。我们提出了一个 新的家庭模型，建立在良好的结构框架的联合盲源分离，以捕捉一个 更完整的表征（潜在的非线性）时空动态。我们的模特也会支持- 引入一组丰富的指标来表征可用的动态，并与当前的 可用的模型。我们有证据表明，这些措施可能更加敏感， 准确地对个体进行分类。我们将以各种方式广泛验证我们的方法，包括 模拟，并发EEG/fMRI数据，并在大型规范性数据集上进行评估。我们将应用开发- 对几个大型数据集进行了优化，其中包括一个大型纵向样本， 在埃默里大学进行静息功能磁共振成像扫描，同时进行CSF淀粉样蛋白和tau PET测量。我们将使用 在这些数据中，开发的预测认知能力下降、淀粉样蛋白和tau水平的标记物包括 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 这是重要的第一步，有助于尽早制定和评估干预措施， 对长期预后有积极影响。我们将提供开源工具，并在整个 通过GitHub，门户网站和NITRC存储库，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，并将其应用于各种各样的大脑疾病。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

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