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

基本信息

批准号：
10375496
负责人：
TULAY ADALI
金额：
$ 63.09万
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：
2021-03-19 至 2026-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10375496
关键词：
Address Algorithms Alzheimer&apos 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 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 diagnostic criteria 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

项目摘要

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

项目概要/摘要现有的根据功能磁共振成像数据估计和表征全脑时变连接的方法已经显示出巨大的前景，该领域的研究呈指数级增长。我们和其他人开发了一个一套强大的工具，现已在社区中广泛使用。然而，精神疾病对大脑的影响连接性很复杂，正如我们所展示的，现有方法的局限性通常会导致丢失重要特征与大脑疾病相关（例如大脑网络空间结构的短暂分裂）。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先和未充分研究的限制性假设，2）方法通常假设线性关系在或网络之间随着时间的推移，3）方法假设空间固定节点并忽略空间固定的可能性网络随时间的流动演变。我们提出了一个新颖的模型系列，该模型建立在结构良好的基础上联合盲源分离框架，以捕获更完整的特征（可能是非线性的）时空动力学，同时提供一种放宽其他限制假设的方法。我们的模型还将生产一组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用模型，包括基于模型的模型。我们将在各种方面广泛验证我们的方法方法包括模拟和评估大型规范数据集的严谨性和鲁棒性。最后，我们将应用已开发的工具来研究精神疾病（包括精神分裂症）动态特性的重要领域恐惧症、双向情感障碍和自闭症谱系。有大量证据表明动力学受到破坏在所有三种疾病中，正如我们所表明的，使用静态（甚至退出动态）方法可能会错过重要的有关与每个相关的大脑相关差异的信息。我们将提供开源工具并发布通过门户网站和 NITRC 存储库在整个项目期间提供数据，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对健康大脑以及许多其他疾病（如阿尔茨海默病）和注意力的研究缺乏性多动症。 38