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

• 批准号: 9297548
• 负责人: TULAY ADALI
• 金额: $ 10.05万
• 依托单位: THE MIND RESEARCH NETWORK
• 依托单位国家: 美国
• 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/9297548
• 关键词: Address; Algorithms; Alzheimer' 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; Health; 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; abstracting; acronyms; associated symptom; base; blind; clinical care; drinking; improved; independent component analysis; infancy; interest; meetings; neuropsychiatry; novel; open source; repository; simulation; spatiotemporal; statistics; success; symposium; tool; user-friendly; vector; web portal

 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的大脑方面已经取得了很大进展。功能磁共振成像社区现在已经意识到，功能连接的评估已被整个测量期间的空间和时间平稳性的隐含假设所限制。动力学在休息状态下可能更加突出，在此期间，心理活动不受约束。需要新的方法来估计和量化这些变化。我们建议开发和比较一个多样化的，但统一的家庭的多变量方法，以解决动态连接的重要方面，目前没有捕获与现有的方法。初步方法的试点数据显示，精神疾病发生了巨大变化。使用一个强大的框架，建立在良好的结构框架的联合盲源分离，我们将利用所有可用的先验和统计信息高阶统计，稀疏性，平滑度，样本和数据集的依赖性，以获得一类新颖的和有效的动态模型的静态和动态的大脑连接的充分表征。我们将验证这些新方法，同时确定它们的属性和对噪声和其他因素的鲁棒性。我们初步的工作表明，有重要的动态性能的变化，是不可检测的静态结果，反之亦然。因此，我们还提出了可以同时捕获静止和非静止活动的模型。我们将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）以及共病条件（吸烟和饮酒）的共同和不同方面。我们将在整个项目期间通过门户网站和NITRC存储库提供开源工具和发布数据，从而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，并将其应用于各种各样的大脑疾病。我们的工具广泛应用于健康大脑的研究以及许多其他疾病，如阿尔茨海默氏症和注意力缺陷多动障碍。37