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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

项目总结/文摘