融合t-分布随机邻域嵌入与自动谱聚类的脑功能精细分区方法

本文针对目前脑功能分区不够准确的问题，基于静息态功能磁共振数据，提出了一种融合t-分布随机邻域嵌入（t-SNE）与自动谱聚类（ASC）的人脑功能精细分区的算法.首先，基于静息态功能磁共振图像，对需功能划分的脑区与全脑的时间序列作相关分析，得到需划分脑区的功能连接模式；然后，利用t-SNE算法提取高维功能连接模式特征；最后，通过基于本征间隙的ASC算法自动确定聚类数目，并对降维后的脑区特征分类，得到精细划分的脑亚区.模拟种子区域上的实验结果表明，相较谱聚类算法，以及结合主成分分析的谱聚类算法，本文方法对脑功能体素划分更优.进一步将本方法应用到真实人脑的功能分区中，成功地将海马旁回分为左右半球各3个亚区.本研究表明使用t-SNE与ASC融合的算法可提高脑功能分区准确性，是脑功能精细分区、进而构建脑功能图谱的一种有效方法.     

Multivariate Gaussian Copula Mutual Information to Estimate Functional Connectivity with Less Random Architecture

Recognition of a brain region’s interaction is an important field in neuroscience. Most studies use the Pearson correlation to find the interaction between the regions. According to the experimental evidence, there is a nonlinear dependence between the activities of different brain regions that is ignored by Pearson correlation as a linear measure. Typically, the average activity of each region is used as input because it is a univariate measure. This dimensional reduction, i.e., averaging, leads to a loss of spatial information across voxels within the region. In this study, we propose using an information-theoretic measure, multivariate mutual information (mvMI), as a nonlinear dependence to find the interaction between regions. This measure, which has been recently proposed, simplifies the mutual information calculation complexity using the Gaussian copula. Using simulated data, we show that the using this measure overcomes the mentioned limitations. Additionally using the real resting-state fMRI data, we compare the level of significance and randomness of graphs constructed using different methods. Our results indicate that the proposed method estimates the functional connectivity more significantly and leads to a smaller number of random connections than the common measure, Pearson correlation. Moreover, we find that the similarity of the estimated functional networks of the individuals is higher when the proposed method is used.     

脑自发性神经振荡低频振幅表征脑功能网络静息态信息流

已有研究表明,任务诱发的局部脑区激活可通过全脑功能网络中脑区间的神经活动信息流（activity flow,AF）预测,然而静息态下自发脑神经振荡与AF的关系仍不清楚。本文旨在研究静息态自发神经活动是否也反映脑区间在功能连接(functional connectivity,FC)路径上的信息传播。用来自千人脑功能连接组计划中的197名健康被试的静息态功能磁共振成像（RS-fMRI）数据,计算全脑160个感兴趣区的自发性神经振荡低频振幅（amplitude of low-frequency fluctuation,ALFF）,采用相关性分析和多元回归分析2种方法计算脑区间FC;基于AF模型,通过ALFF与FC的加权和估计汇聚到目标脑区的AF,利用Pearson相关性分析在全脑层面和默认网络脑区层面ALFF与AF之间的空间相关性。结果表明,在全脑层面和默认网络（default-mode network,DMN）脑区层面,AF与ALFF的分布模式显著相关;用多元回归改进FC估计可改善预测效果。低频振幅不仅体现脑区局部的神经振荡和功能情况,同时也反映自发性脑神经活动通过FC通道在脑区间进行信息交互。     

静息态脑电信号动态功能连接分析

静息态脑功能连接分析是近年来脑研究的一个热点问题, 对于某些脑疾病的诊断及成因理解具有重要意义. 已有的脑功能连接研究基本上都假设功能连接网络在一段时间内是稳定不变的, 但越来越多的证据表明它应该是随时间动态变化的. 对25名被试睁眼和闭眼状态的64电极脑电生理信号, 采用独立成分分析、滑动时间窗、低分辨率脑电断层溯源、图论等方法和技术进行动态功能连接分析, 展现了睁眼和闭眼两种基线状态下视觉网络、默认网络等功能连接网络随时间的动态变化, 并对动态连接矩阵进行主成分分析得到了在整个时间段内具有代表意义的功能连接模式. 该结论支持和补充了传统稳态脑功能连接的研究, 也将为相关实验设计以及脑电信号临床研究提供基线选择依据.