Data mining in large functional neuroimaging databases

大型功能神经影像数据库中的数据挖掘

• 批准号: 436141-2013
• 负责人: Bellec, Pierre
• 金额: $ 1.09万
• 依托单位: Université de Montréal
• 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=634269
• 关键词: Data; mining; large; functional; neuroimaging

Functional MRI gives an indirect measure of brain activity with a good temporal and spatial resolution. This massive amount of information needs to be summarized to be interpretable. Cluster analysis can identify spatially distributed regions with a temporally correlated activity at rest, called resting-state networks (RSNs). The overarching goal of my research program is to capture how the connectivity between these RSNs changes in response to Alzheimer's disease. The short-term objectives of this proposal target the technical challenges raised by this long-term goal: (1) The number (and size) of RSNs is an arbitrary choice of the investigator, i.e. the brain can be decomposed into large distributed systems as well as into specialized brain regions. I want to determine which level of decomposition is the most optimal to detect the effects of Alzheimer's disease on brain connectivity. (2) Currently, RSNs are derived at the level of a group after the brains of all subjects have been aligned based on their morphology. The functional units of the brain can still present variations amongst individuals after this process. We will develop novel clustering techniques that will match this remaining functional variability across subjects. This operation is expected to reveal better the deleterious effects of Alzheimer's disease on brain connectivity, that could have been masked by the functional misregistration between subjects; (3) The generation of RSNs and associated connectivity measures is a long process involving many steps and decisions. We will systematically test the impact of various choices made in the preprocessing of fMRI datasets on the outcomes of Objectives 1 & 2, using both real and simulated datasets. I anticipate that the developments made in this research program will substantially increase the reliability and sensivity of outcome measures based on resting-state fMRI with important implications for both clinical and basic neuroscience.

功能磁共振成像提供了一个间接的测量大脑活动具有良好的时间和空间分辨率。这些大量的信息需要被总结以便于解释。聚类分析可以识别空间分布的区域与时间相关的活动在休息，称为休息状态网络（RSN）。我的研究计划的首要目标是捕捉这些RSN之间的连接如何响应阿尔茨海默病的变化。该提案的短期目标针对这一长期目标提出的技术挑战：（1）RSN的数量（和大小）是研究者的任意选择，即大脑可以分解为大型分布式系统以及专门的大脑区域。我想确定哪种分解水平是检测阿尔茨海默病对大脑连接影响的最佳水平。(2)目前，RSN是在所有受试者的大脑基于其形态学进行对齐后，在组的水平上得出的。在这个过程之后，大脑的功能单位仍然可以呈现个体之间的差异。我们将开发新的聚类技术，以匹配受试者之间剩余的功能变异性。这种操作有望更好地揭示阿尔茨海默病对大脑连接的有害影响，这可能被受试者之间的功能失调所掩盖;（3）RSN和相关连接性测量的生成是一个涉及许多步骤和决策的漫长过程。我们将使用真实的和模拟的数据集，系统地测试在fMRI数据集预处理中所做的各种选择对目标1和2的结果的影响。我预计，在这项研究计划中取得的进展将大大增加的可靠性和灵敏度的结果措施的基础上休息状态功能磁共振成像的临床和基础神经科学的重要影响。