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: 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
• 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' 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

 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(申请人提供)：在使用功能连通性测量来研究健康和疾病的大脑方面取得了很大的进展。功能磁共振成像社区现在已经意识到，功能连通性的评估已经受到整个测量期间空间和时间平稳性的隐含假设的限制。在静息状态下，动力学甚至可能更加突出，在此期间，精神活动不受限制。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不同但统一的多变量方法家族，以解决动态连接的重要方面，这些方面目前无法用现有方法捕获。初步方法的试点数据显示，精神疾病的变化很大。利用建立在结构良好的联合盲源分离框架上的强大框架，我们将利用所有可用的先验和统计信息-高阶统计量、稀疏性、平滑性、样本和数据集相关性来推导出一类新颖而有效的动态模型，用于全面表征静态和动态的大脑连接。我们将验证这些新方法，同时确定它们的性质以及对噪声和其他因素的稳健性。我们的初步工作表明，在静态结果中无法检测到的动态性质存在重要的变化，反之亦然。因此，我们还提出了能够同时捕捉平稳和非平稳活动的模型。我们将应用我们的一套新方法来评估两个患者组(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。我们将提供开源工具，并在整个项目期间通过门户网站和NITRC储存库发布数据，从而使其他研究人员能够将他们自己的方法与我们的方法进行比较，并将它们应用于各种大脑疾病。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，如阿尔茨海默氏症和注意力缺陷多动障碍。37