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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636964
• 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 toclinically-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 ApproachMost 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 willprovide a framework for multimodal assessment of brain activity, with widespread potential impact in the assessment of normal brain functioning and in disease states.

拟议研究计划的目标：这项研究将继续申请者在统计信号处理应用于临床相关数据方面的工作。长期目标是开发新的、基本的统计信号处理方法，这些方法将对临床脑成像领域产生持久的影响。为了实现这一目标，这项提议侧重于解决两个基本问题：数据融合：成像方式之间以及成像与临床和遗传数据之间的数据融合；以及大脑连接的动力学方面。现代成像技术重新点燃了一场关于大脑活动如何代表的数百年来的辩论：在长期假设与特定任务相关的大脑活动是局部性的之后，现在人们对不同大脑区域如何共同激活有了更大的认识。空间上不同的大脑区域之间持续的、动态的关联似乎对正常的大脑功能至关重要，而这些连接模式的中断是疾病的敏感标志。因此，准确评估大脑连通性模式是这一提议的首要目标。科学方法大多数检测大脑连通性的分析都是针对一种技术(如功能磁共振成像、脑电波)的，每种技术在时空平面上都占据了有限的区域。组合不同的脑模式以提供在多个时间和空间尺度上的大脑连通性的综合评估不是微不足道的，因为每种模式衡量不同的生物活动(例如，电活动与血液流动的变化)，并且具有不同的统计特征。我们将扩大我们之前在功能磁共振成像、EEG-EEG和EEG-EMG连通性方面的工作，以及用线性动力系统模型表征同时记录的行为数据，以便将这些互补信息合并在一起，为疾病过程提供敏感和特异的标记。遗传影响在帕金森氏病等神经退行性疾病中的作用日益得到认识。携带基因突变的人患疾病的风险很高，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，对大家庭中的大脑连接模式进行分类可能会表明哪些成员拥有共同的疾病相关表型，从而大大缩小了对与AS特定疾病相关的基因相关变化的搜索范围。然而，基因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在一起，我们将扩展我们在稀疏回归和稀疏精度矩阵方面的工作。目前大多数大脑连接模型假设任务执行过程中连接模式的平稳性。然而，大脑在本质上是非静态的，对动态变化的连通性模式进行建模仍处于起步阶段。从我们之前在功能磁共振数据集中评估连通性的工作开始，我们将使用一个动态框架来探索任务执行过程中连通性模式的确定性变化。工作的新颖和预期意义：除了为HQP的跨学科培训提供一个竞争激烈的环境外，这项工作还将提供一个多模式大脑活动评估的框架，在评估正常大脑功能和疾病状态方面具有广泛的潜在影响。