Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivity

用于时间、空间和时空动态功能连接的数据驱动解决方案

• 批准号: 10559654
• 负责人: TULAY ADALI
• 金额: $ 59.5万
• 依托单位: GEORGIA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-19 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10559654
• 关键词: Address; Algorithms; Alzheimer' s Disease; Area; Attention deficit hyperactivity disorder; Back; Behavior; Benchmarking; Bipolar Disorder; Brain; Brain Diseases; Classification; Communities; Complex; Computer software; Coupled; Coupling; Data; Data Set; Dimensions; Disease; Documentation; Ensure; Evaluation; Evolution; Family; Fingerprint; Fractionation; Frequencies; Functional Magnetic Resonance Imaging; Goals; Growth; Individual; Joints; Liquid substance; Mental disorders; Methods; Modeling; Moods; Nonlinear Dynamics; Patients; Property; Psychoses; Relaxation; Reproducibility; Research; Research Personnel; Schizophrenia; Source; Structure; Study models; Subgroup; Symptoms; Testing; Time; Validation; Work; autism spectrum disorder; blind; clinical care; clinically relevant; deep learning; diagnostic criteria; flexibility; interest; model building; multimodal data; neuropsychiatric disorder; novel; novel strategies; open source; open source tool; rapid growth; repository; simulation; social; social cognition; spatial integration; spatiotemporal; tool; translational impact; user-friendly; web portal

Project Summary/Abstract Existing approaches to estimate and characterize whole brain time-varying connectivity from fMRI data have shown considerable promise, with exponential growth in research in this field. We and others have developed a powerful set of tools that are now in wide use in the community. However, the impact of mental illness on brain connectivity is complex, and as we show, limitations in existing methods often result in missing important features associated with brain disorders (e.g. transient fractionation of the spatial structure of brain networks). Some of these important limitations include 1) the most widely-used approaches often require a number of prior and limiting assumptions that are not well studied, 2) methods often assume linear relationships either within or between networks over time, and 3) methods assume spatially fixed nodes and ignore the possibility of spatially fluid evolution of networks over time. 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 while providing a way to relax other limiting assumptions. Our models will also produce a rich set of metrics to characterize the available dynamics and enable in depth comparison with currently avail- able models including those that are model based. We will extensively validate our approaches in a variety of ways including simulations and evaluation of rigor and robustness in large normative data sets. Finally, we will apply the developed tools to study the important area of dynamic properties in mental illnesses including schiz- ophrenia, bipolar disorder, and the autism spectrum. There is considerable evidence of disruption of dynamics in all three disorders, and as we show the use of static (or even exiting dynamic) approaches can miss important information about brain related differences associated with each. We will provide open source tools and release data throughout the duration of the project via a web portal and the NITRC repository, hence enabling other investigators to use our approaches and compare their own methods with our own. Our tools have wide appli- cation to the study of the healthy brain as well as many other diseases such as Alzheimer's disease and attention deficit hyperactivity disorder. 38

项目总结/摘要 从fMRI数据估计和表征全脑时变连通性的现有方法 显示出相当大的前景，在这一领域的研究呈指数增长。我们和其他人开发了一种 一套强大的工具，现在在社区中广泛使用。然而，精神疾病对大脑的影响 连通性是复杂的，正如我们所展示的，现有方法的局限性往往导致重要特征的缺失 与脑部疾病相关（例如，大脑网络空间结构的短暂分离）。一些 这些重要的限制包括：1）最广泛使用的方法通常需要许多先验知识， 限制性假设没有得到很好的研究，2）方法通常假设线性关系，或者 3）方法假设空间上固定的节点，忽略空间上的可能性， 随着时间的推移，网络的流体演化。我们提出了一个新的家庭的模型，建立在良好的结构， 联合盲源分离的框架，以捕获（潜在非线性）的更完整表征 时空动态，同时提供一种放松其他限制性假设的方法。我们的模特还将制作 一组丰富的指标来表征可用的动态，并能够与当前可用的进行深入比较， 有能力的模型，包括那些基于模型的。我们将广泛验证我们的方法在各种 方法，包括模拟和大型规范数据集的严谨性和鲁棒性的评估。最后我们将 应用开发的工具来研究精神疾病，包括精神分裂症的动态特性的重要领域， 抑郁症，躁郁症，和自闭症谱系。有相当多的证据表明， 在所有这三种疾病中，正如我们所展示的那样，使用静态（甚至是现有的动态）方法可能会错过重要的 关于大脑相关差异的信息。我们将提供开源工具并发布 在整个项目期间，通过门户网站和NITRC存储库收集数据，从而使其他 研究人员使用我们的方法，并将他们自己的方法与我们的方法进行比较。我们的工具有广泛的应用- 阳离子的研究健康的大脑以及许多其他疾病，如阿尔茨海默氏症和注意力 缺陷多动症 38