Unified multivariate data-driven solutions for static and dynamic brain connectivity

用于静态和动态大脑连接的统一多变量数据驱动解决方案

基本信息

批准号：
9283545
负责人：
TULAY ADALI
金额：
$ 73.93万
依托单位：
THE MIND RESEARCH NETWORK
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-15 至 2019-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9283545
关键词：
Address Algorithms Alzheimer&apos s Disease Attention deficit hyperactivity disorder Bipolar Disorder Brain Brain Diseases Classification Communities Comorbidity Computer software Coupled Coupling Data Data Set Dependence Diagnosis Disease Documentation Electroencephalography Ensure Family Frequencies Functional Magnetic Resonance Imaging Goals Growth Impact evaluation Joints Measurement Measures Mental disorders Methods Modeling Noise Patients Pattern Property Psyche structure Psychotic Disorders Reporting Research Personnel Rest Sampling Schizophrenia Series Smoking Source Structure Study models Symptoms Temporal Lobe Testing Time Validation Work acronyms associated symptom base blind clinical care drinking improved independent component analysis infancy interest meetings neuropsychiatry novel open source public health relevance repository simulation spatiotemporal statistics symposium tool translational impact user-friendly vector web portal

项目摘要

DESCRIPTION (provided by applicant): There has been great progress in the use of functional connectivity measures to study the healthy and dis- eased brain. The fMRI community has now realized that assessment of functional connectivity has been limited by an implicit assumption of spatial and temporal stationarity throughout the measurement period. Dynamics are potentially even more prominent in the resting-state, during which mental activity is unconstrained. There is a need for new methods to both estimate and quantify these changes. We propose to develop and compare a diverse but unified family of multivariate methods to address important aspects of dynamic connectivity that are not presently captured with existing approaches. Pilot data with initial approaches show robust changes in mental illness. Using a powerful framework that builds on the well-structured framework of joint blind source separation, we will make use of all available prior and statistical information-higher-order-statistics, sparsity, smoothness, sample and dataset dependence to derive a class of novel and effective dynamic models for full characterization of static and dynamic brain connectivity. We will validate these new methods while determining their properties and robustness to noise and other factors. We show preliminary work suggesting that there are important changes in dynamic properties that are not detectable in the static results and vice versa. Thus, we also propose models that can simultaneously capture stationary and non-stationary activity. We will apply our new set of methods to evaluate the common and distinct aspects of two patient groups (schizophrenia and bipolar disorder) as well as comorbid conditions (smoking and drinking). 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 compare their own methods with our own as well as to apply them to a large variety of brain disorders. Our tools have wide application to the study of the healthy brain as well as many other diseases such as Alzheimer's and attention deficit hyperactivity disorder. 37

描述（由申请人提供）：在使用功能连接测量来研究健康和患病大脑方面已经取得了巨大进展。功能磁共振成像界现在已经意识到，功能连接的评估受到了空间和疾病的隐含假设的限制。整个测量期间的时间平稳性在静息状态下可能更加突出，在此期间心理活动不受限制，我们建议开发和比较不同的方法。团结的家庭多变量方法来解决现有方法目前无法捕获的动态连接的重要方面，初步数据显示精神疾病发生了巨大的变化，我们将使用建立在结构良好的联合盲源分离框架之上的强大框架。利用所有可用的先验和统计信息（高阶统计、稀疏性、平滑性、样本和数据集依赖性）来导出一类新颖且有效的动态模型，以全面表征静态和动态大脑连接性。同时确定其属性和稳健性我们的初步工作表明，静态结果中无法检测到动态特性的重要变化，因此，我们还提出了可以同时捕获静态和非静态活动的模型。应用我们的一套新方法来评估两个患者群体（精神分裂症和双相情感障碍）的共同和不同方面以及合并症（吸烟和饮酒），我们将在项目期间提供开源工具并通过以下方式发布数据。门户网站和NITRC 存储库，因此使其他研究人员能够将他们自己的方法与我们自己的方法进行比较，并将其应用于各种脑部疾病，我们的工具广泛应用于健康大脑以及许多其他疾病的研究。阿尔茨海默病和注意力缺陷多动障碍 37。