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

该奖项的目标是开发一种计算框架，用于识别神经成像数据中大脑连接的关键网络拓扑，特别是功能磁共振成像（fMRI）。在这个框架中，网络优化建模和数学规划算法将被用于表征来自不同大脑区域的fMRI数据的连接模式。机器学习技术将用于构建模式识别模型，用于检测生物标志物并预测大脑疾病状况（即异常与对照）。利用信息论的方法来选择信息量最大的脑区，以提高诊断预测模型的泛化性和准确性。如果成功，本研究结果将导致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。开发的计算框架将对神经科学家和神经学家识别大脑中的异常功能连接以及更好地了解大脑功能非常有用。该框架将作为一种新的生物标志物用于脑部疾病的鉴别诊断。老年痴呆症吗?s病（AD）、自闭症谱系障碍（ASD）和帕金森？该项目将以帕金森病（PD）为例，测试我们的计算框架是否足够敏感，能够检测与大脑疾病相关的大脑连接变化。准确的诊断可以大大延长病人的寿命。一些治疗方法在不同的疾病阶段有不同的效果。此外，开发的计算框架可以应用于其他研究领域（如制造业、医学、生物信息学、神经科学、金融和地球科学）中出现的其他现实生活中的大规模时空数据。

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.