Network Optimization of Functional Connectivity in Neuroimaging for Differential Diagnoses of Brain Diseases

神经影像功能连接的网络优化用于脑部疾病的鉴别诊断

• 批准号: 1742031
• 负责人: Wanpracha Chaovalitwongse
• 金额: $ 4.02万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1742031&HistoricalAwards=false
• 关键词: Network; Optimization; Functional; Connectivity; Neuroimaging

The objective of this award is to develop a computational framework for identifying the critical network topology of brain connectivity in neuroimaging data, specifically functional magnetic resonance imaging (fMRI). In this framework, network optimization modeling and mathematical programming algorithms will be employed to characterize connectivity patterns in fMRI data from different brain regions. Machine learning techniques will be employed to construct a pattern recognition model used to detect biomarkers and predict the brain disease conditions (i.e., abnormals vs. controls). An information-theoretic approach will be used to select the most informative brain regions to improve the generalizability and to increase the accuracy of the diagnosis prediction model.If successful, the results of this research will lead to improvements in efficiency and efficacy of brain functional connectivity modeling and new developments of optimization methods for handling large-scale spatio-temporal data. The developed computational framework will be extremely useful for neuroscientists and neurologists to identify abnormal functional connectivity in the brain and to gain a greater understanding of the brain function. The framework will be employed and tested as a novel biomarker for differential diagnoses of brain disorders. Alzheimer?s disease (AD), autism spectrum disorder (ASD), and Parkinson?s disease (PD) will be the case points in this project to test if our computational framework is a sensitive enough tool to detect alterations in brain connectivity associated with brain disorders. Accurate diagnosis can substantially extend a patient?s lifespan and some treatments have different outcomes at different disease stages. Additionally, the developed computational framework can be applied to other real-life large-scale spatio-temporal data that arise in other research areas such as manufacturing, medicine, bioinformatics, neuroscience, finance, and geosciences.

该奖项的目的是开发一个计算框架，以识别神经影像数据中大脑连通性的关键网络拓扑，特别是功能磁共振成像（fMRI）。在此框架中，将采用网络优化建模和数学编程算法来表征来自不同大脑区域的fMRI数据中的连接模式。机器学习技术将被用来构建用于检测生物标志物并预测脑部疾病状况的模式识别模型（即异常与对照）。信息理论方法将用于选择最有用的大脑区域，以提高普遍性并提高诊断预测模型的准确性。开发的计算框架对于神经科学家和神经科医生来说将非常有用，以鉴定大脑中异常的功能连通性并对大脑功能有了更大的了解。该框架将被用作脑部疾病鉴别诊断的新型生物标志物。阿尔茨海默氏病（AD），自闭症谱系障碍（ASD）和帕金森氏病（PD），该项目中的情况将是测试我们的计算框架是否足以检测与脑部疾病相关的大脑连通性改变的敏感工具。准确的诊断可以实质上延长患者的寿命，并且某些疗法在不同的疾病阶段的结局不同。此外，开发的计算框架可以应用于其他研究领域（例如制造，医学，生物信息学，神经科学，金融和地球科学）等其他研究领域中出现的其他现实大规模时空数据。