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（由适用提供）：使用功能连通性措施来研究健康和疾病的大脑，取得了长足的进步。 FMRI社区现在已经意识到，在整个测量期间，由空间和临时平稳性的隐含假设限制了功能连通性的评估。在静息状态下，动态可能更加突出，在此期间，精神活动不受限制。需要新方法来估计和量化这些变化。我们建议开发和比较一种不同但统一的多元方法家族，以解决动态连通性的重要方面，这些方面未通过现有方法捕获。初始方法的飞行员数据表明，精神疾病的强大变化。使用强大的框架，该框架建立在结构良好的盲源分离框架上，我们将利用所有可用的先前和统计信息高阶统计数据，稀疏性，平滑度，样本和数据集依赖性来得出一类新颖有效的动态模型，以完全表现静态和动态大脑连接性。我们将验证这些新方法，同时确定噪声和其他因素的鲁棒性和鲁棒性。我们展示了初步工作，表明动态特性存在重要变化，而静态结果中无法检测到，反之亦然。这是我们提出的模型，可以轻松捕获固定和非平稳活动。我们将应用新的方法来评估两个患者组（精神分裂症和躁郁症）的常见和不同方面以及合并症（吸烟和饮酒）。我们将通过Web门户和NITRC存储库在整个项目期间提供开源工具和发布数据，从而使其他研究人员能够将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脑部疾病。我们的工具广泛地用于研究健康大脑以及许多其他疾病，例如阿尔茨海默氏症和注意力缺陷多动障碍。 37