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：
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
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/10559654
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 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

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

项目摘要/摘要现有的方法来估计和表征来自fMRI数据的整个大脑时变连通性在该领域的研究中表现出了巨大的希望。我们和其他人开发了现在在社区中广泛使用的强大工具集。但是，精神疾病对大脑的影响连通性很复杂，正如我们所显示的，现有方法中的局限性通常会导致缺失的重要功能与脑疾病有关（例如，脑网络空间结构的瞬时分馏）。其中一些这些重要的局限性包括1）使用最广泛的方法通常需要一些先验和限制研究未进行充分研究的假设，2）方法通常假设内部或在网络之间随着时间的推移，3）方法假设空间固定的节点，并忽略了空间的可能性随着时间的推移，网络的流体演变。我们提出了一个新颖的模型家族，建立在结构良好的基础上联合盲源分离的框架以捕获更完整的表征（潜在的非线性）时空动力学，同时提供了放松其他限制假设的方法。我们的模型也将产生一组丰富的指标来表征可用的动态，并与当前可用包括基于模型的模型在内的模型。我们将广泛验证我们的方法包括模拟和评估严格和鲁棒性的方式，包括大型规范性数据集。最后，我们会的应用开发的工具来研究精神疾病中动态特性的重要领域 oprenia，躁郁症和自闭症谱系。有大量动态破坏的证据在所有三种疾病中，当我们显示静态（甚至是退出动态）方法的使用时，可能会错过重要的有关大脑相关的差异的信息。我们将提供开源工具并发布通过Web门户和NITRC存储库在项目的整个过程中的数据，因此可以实现其他调查人员使用我们的方法并将自己的方法与我们自己的方法进行比较。我们的工具具有广泛的应用阳离健康大脑以及许多其他疾病，例如阿尔茨海默氏病和注意力赤字多动障碍。 38