Assessment of multi-modal, genetically influenced, dynamic brain connectivity in disease states

疾病状态下多模式、遗传影响、动态大脑连接的评估

• 批准号: 435991-2013
• 负责人: McKeown, Martin
• 金额: $ 2.11万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=574254
• 关键词: Assessment; multi; modal; genetically; influenced

Objects of the Proposed Research Program: This research will continue the applicant's work on statistical signal processing applications applied to clinically-relevant data. The long term goal is to develop novel, fundamental statistical signal processing approaches that will have a lasting impact on the fields of clinical brain imaging. Toward this objective, this proposal focuses on tackling two fundamental topics: data fusion: between imaging modalities as well as between imaging and clinical and genetic data; and dynamical aspects of brain connectivity. Modern imaging technologies has re-ignited a centuries-old debate about how brain activity is represented: after long assuming that brain activity associated with specific tasks is localized, there is now a greater appreciation on how diverse brain areas co-activate. Ongoing, dynamic association between spatially disparate brain regions appears critical for normal brain functioning and disruption of these connectivity patterns is a sensitive marker for disease. Accurate assessment of brain connectivity patterns is thus an overarching objective of this proposal. Scientific Approach Most analyses examining brain connectivity have been specific to a technology (e.g. fMRI, EEG), each of which occupies a limited area in the spatiotemporal plane. Combining different brain modalities to provide a comprehensive assessment of brain connectivity at multiple temporal and spatial scales is non-trivial, as each modality measures different biological activity (e.g. electrical activity vs. changes in blood flow), and has different statistical characteristics. We will expand our prior work on fMRI, EEG-EEG and EEG-EMG connectivity, as well as characterization of simultaneously-recorded behavioral data with linear dynamical system models, so that this complementary information can be merged together to provide sensitive and specific markers for disease processes. The role of genetic influences in neurodegenerative diseases such as Parkinson's disease is being increasingly recognized. People with genetic mutations putting them at high risk for developing disease provide a rare opportunity to examine connectivity changes before symptoms emerge. In complementary fashion, classifying brain connectivity patterns within an extended family may suggest which members share a common disease-related phenotype, substantially narrowing the search for gene-related alterations associated with as specific disease. However, both genetic and imaging data suffer from common challenges: the data are inherently high-dimensional with relatively few available samples. In order to investigate how genetic and imaging data may be meaningfully combined, we will extend our work on sparse regression and sparse precision matrices. Most current models of brain connectivity assume stationarity of connectivity patterns during performance of a task. However the brain in inherently non-stationary, and modeling dynamic changes in connectivity patterns is still in its infancy. Starting with our prior work on assessing connectivity in fMRI data sets, we will employ a dynamical framework to explore deterministic alterations in connectivity patterns during task performance. Novelty and Expected Significance of Work: Besides providing a highly competitive environment for interdisciplinary training of HQP, this work will provide a framework for multimodal assessment of brain activity, with widespread potential impact in the assessment of normal brain functioning and in disease states.

拟议研究计划的对象： 这项研究将继续申请人的工作，统计信号处理的应用， 临床相关数据。长期目标是开发新的，基本的统计信号处理方法，这将对临床脑成像领域产生持久的影响。为了实现这一目标，该提案侧重于解决两个基本问题：数据融合：成像模式之间以及成像与临床和遗传数据之间;以及大脑连接的动态方面。 现代成像技术重新引发了一场关于大脑活动如何表现的争论：在长期假设与特定任务相关的大脑活动是局部的之后，现在人们对不同的大脑区域如何共同激活有了更大的了解。空间上不同的大脑区域之间持续的动态关联对于正常的大脑功能至关重要，这些连接模式的破坏是疾病的敏感标志。因此，准确评估大脑连接模式是本提案的首要目标。 科学方法 大多数研究大脑连接的分析都是特定于一种技术（例如fMRI，EEG）的，每种技术在时空平面上占据有限的区域。结合不同的脑模态以在多个时间和空间尺度上提供对脑连通性的全面评估是不平凡的，因为每种模态测量不同的生物活动（例如，电活动与血流变化），并且具有不同的统计特征。我们将扩展我们先前在fMRI，EEG-EEG和EEG-EMG连接方面的工作，以及用线性动力学系统模型表征错误记录的行为数据，以便将这种互补信息合并在一起，为疾病过程提供敏感和特异性的标记。 遗传影响在神经退行性疾病如帕金森病中的作用正日益被认识到。具有基因突变的人使他们处于患病的高风险中，这为在症状出现之前检查连接变化提供了难得的机会。以互补的方式，在一个大家庭中对大脑连接模式进行分类可能会表明哪些成员具有共同的疾病相关表型，从而大大缩小了与特定疾病相关的基因相关改变的搜索范围。然而，遗传和成像数据都面临着共同的挑战：数据本身就是高维的，可用的样本相对较少。为了研究遗传和成像数据如何有意义地结合起来，我们将扩展我们在稀疏回归和稀疏精度矩阵上的工作。 大多数目前的大脑连接模型假设在执行任务的连接模式的平稳性。然而，大脑在本质上是非静止的，并且建模连接模式的动态变化仍处于起步阶段。从我们以前的工作评估功能磁共振成像数据集的连接，我们将采用一个动态的框架，探索确定性的改变连接模式在任务执行。 工作的新奇性和预期意义： 除了为HQP的跨学科培训提供高度竞争的环境外，这项工作还将 为大脑活动的多模态评估提供了一个框架，在正常大脑功能和疾病状态的评估中具有广泛的潜在影响。