多模态fMRI信息融合的脑功能网络构建与分析

项目名称： 多模态fMRI信息融合的脑功能网络构建与分析

项目编号： No.61272267

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 何良华

作者单位： 同济大学

项目金额： 81万元

中文摘要： 随着核磁共振成像质量的不断提高和最新研究的跨越式进展，研究人员可精确地定位与细分不同脑区的功能。近年来，脑功能性连接已成为脑认知研究的热点，主要通过分析任务态或静息态等单一状态下所获取的脑认知数据来完成，所得脑网络结果或者具有较强的任务依赖，或者难以获得认知解释。因此，本课题提出任务与静息多模态下脑功能网络的研究思路，利用各任务态下认知功能明确、静息态下功能默认的特点，综合研究脑网络模型，探讨认知计算。主要研究内容包括：基于小波压缩传感空间的海量脑影像数据合理高效降维研究；基于频率的多模态fMRI中有效脑激活特征研究；基于贝叶斯网络的静息态脑网络建模与分析；基于多模态fMRI的半监督脑网络模型研究;开展各脑网络模型认知表达与各模态功能核磁共振成像数据认知特点的研究；在上述认知模型与认知数据关系研究的基础上，探索认知计算的基本机理。本课题的开展，既全面研究脑网络构建，还深度理解脑认知。

中文关键词： 儿童多动症；核磁共振成像；贝叶斯网络；深度神经网络；卷积神经网络

英文摘要： As the improvement of magnetic resonance imaging quality and the greatest development on latest research, researchers can accurately locate and define different brain areas. In recent years, brain functional connectivity has become the hot spots of brain cognitive research, mainly through the analysis of brain cognitive data obtained in the single state of the task state or resting state. The proceeds of the brain network results or have strong task dependence or lack of access to cognitive explanation.The calculated brain networks are depends on the task that have been used greatly or are very difficutly explained in brain cognition. Therefore,the idea of multi-modal combination is proposed in this project.That is, the data from tasking state and resting state is used at the same time. based on the characters of clarity cognition in tasking and function default in resting, we investigate the model of brain network and cognition computing.The main contents include: a rational and efficient dimensionality reduction study on mass brain image data based on wavelet compressive sensing space; The study of effective brain activation characteristics based on the frequency domain of multi-modal fMRI; Modeling and analysis of resting state brain network based on Bayesian networks. The study of semi-supervised brain netwo

英文关键词： ADHD；MRI；Bayesian Network；Deep Neural Network；Convolutional Neural Network