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: 10156006
• 负责人: TULAY ADALI
• 金额: $ 69.34万
• 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/10156006
• 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; Diagnostic; 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; Reproducibility; Research; Research Personnel; Schizophrenia; Source; Structure; Study models; Subgroup; Symptoms; Testing; Time; Validation; Work; autism spectrum disorder; base; blind; clinical care; clinically relevant; deep learning; flexibility; interest; 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

项目摘要/摘要 现有的从fMRI数据估计和表征全脑时变连接性的方法有 显示出相当大的前景，这一领域的研究呈指数级增长。我们和其他人已经开发出一种 一组强大的工具，现在社区中广泛使用。然而，精神疾病对大脑的影响 连通性是复杂的，正如我们所展示的那样，现有方法中的限制往往会导致遗漏重要功能 与大脑紊乱有关(例如，大脑网络空间结构的一过性分裂)。其中一些 这些重要的限制包括1)最广泛使用的方法通常需要大量的事前和 限制没有得到很好研究的假设，2)方法通常假设线性关系，或者在或 3)方法假设节点在空间上是固定的，而忽略了空间上 随着时间的推移，网络的不断演变。我们提出了一个新的模型家族，它建立在结构良好的 捕获(潜在非线性)更完整特征的联合盲源分离框架 时空动力学，同时提供了一种放松其他限制性假设的方法。我们的模型也将生产 一套丰富的指标来描述可用的动态，并能够与当前可用的进行深入比较- 可行的模型，包括那些基于模型的模型。我们将在各种情况下广泛验证我们的方法 方法包括在大型标准数据集中模拟和评估严格性和稳健性。最后，我们会 应用所开发的工具研究精神疾病的动态特性的重要领域，包括精神分裂症。 精神分裂症、双相情感障碍和自闭症。有相当多的证据证明了动力学的颠覆 在所有这三种障碍中，正如我们所展示的那样，静态(甚至退出动态)方法的使用可能会错过重要的 关于大脑相关差异的信息，每种差异都与之相关。我们将提供开源工具并发布 通过网络门户和NITRC储存库在整个项目期间提供数据，从而实现其他 调查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。我们的工具应用广泛- 阳离子对健康大脑以及许多其他疾病的研究，如阿尔茨海默病和注意力 缺乏性多动障碍。 38