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: 1333841
• 负责人: Wanpracha Chaovalitwongse
• 金额: $ 34.5万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1333841&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，用于识别神经成像数据中大脑连接的关键网络拓扑结构，特别是功能性磁共振成像（fMRI）。在这个框架中，网络优化建模和数学规划算法将被用来表征不同脑区的功能磁共振成像数据的连接模式。将采用机器学习技术来构建用于检测生物标志物和预测脑疾病状况的模式识别模型（即，异常与对照）。本研究将利用信息论的方法来选择信息量最大的大脑区域，以提高诊断预测模型的泛化能力和准确性，如果成功的话，本研究的结果将导致大脑功能连接建模的效率和功效的提高，以及处理大规模时空数据的优化方法的新发展。开发的计算框架将是非常有用的神经科学家和神经学家，以确定异常的功能连接在大脑中，并获得更好的了解大脑功能。该框架将作为一种新的生物标志物用于脑疾病的鉴别诊断。老年痴呆症？的疾病（AD），自闭症谱系障碍（ASD），帕金森？的疾病（PD）将在这个项目中的情况下，以测试我们的计算框架是一个足够敏感的工具，以检测与大脑疾病相关的大脑连接的改变。准确的诊断可以大大延长病人？的寿命和一些治疗在不同的疾病阶段有不同的结果。此外，开发的计算框架可以应用于其他现实生活中的大规模时空数据，出现在其他研究领域，如制造业，医学，生物信息学，神经科学，金融和地球科学。