基于静息态功能连接模式的正常人脑扣带皮层亚区划分

基于静息态功能连接(rsFC)模式对正常人脑扣带皮层进行亚区划分,并分析不同亚区的功能连接模式。方法 收集47名右利手健康志愿者。采集结构磁共振图像及静息态功能磁共振图像。在蒙特利尔脑研究所标准空间勾画扣带皮层ROI。基于个体计算ROI中每个体素与全脑其他体素时间序列的Pearson线性相关系数,得到互相关矩阵,采用K-均值聚类算法自动聚类。采用交互验证的方法选择合适分割数。最终计算最大概率图谱。通过单样本t检验确定与每个亚区具有正功能连接的脑区。结果 扣带皮层被分为6个亚区:前扣带皮层、背侧中前扣带皮层、腹侧中前扣带皮层、中后扣带皮层、背侧后扣带皮层及腹侧后扣带皮层亚区。每个亚区有特异的静息态功能连接模式。结论 人脑扣带皮层依据不同的静息态功能连接模式分为6个亚区,每个亚区分属不同的脑功能网络,参与不同的功能。

Parcellation of human cingulate cortex based on resting-state functional connectivity

Objective To parcellate human cingulate cortex (CC) based on resting-state functional connectivity (rsFC) patterns, and to analyze the rsFC patterns of different subregions. Methods Forty-seven healthy, right-handed subjects were enrolled. Structural MRI and resting-state fMRI data were acquired. The ROI of the CC was delineated manually in the Montreal Neurological Institute (MNI) space. For each individual dataset, Pearson correlation coefficients between the time series of each voxel within the ROI and that of each voxel of the whole brain were computed. Cross-correlation matrix of each ROI voxel was calculated and fed into a K-means clustering algorithm for automatic parcellation. Cross-validation was used to determine the number of clusters. Then the maximum probability map for each CC subregion was calculated. A random effect one-sample t test was used to calculate the positive rsFC map of each subregion. Results The CC were divided into 6 subregions: Anterior cingulate cortex, dorsal anterior mid-cingulate cortex, ventral anterior mid-cingulate cortex, posterior mid-cingulate cortex, dorsal posterior cingulate cortex, ventral posterior cingulate cortex. Each subregion showed its specific rsFC pattern. Conclusion The human CC can be divided into six separable subregions according to different rsFC patterns. These subregions are involved in different brain functional networks to serve different functions.