摘要
研究抑郁症患者和正常人在不同的情绪认知任务下大脑功能网络是否存在差异。采用了相位同步的方法以衡量不同脑区间的连接强度，运用网络参数全局和局部效率来刻画大脑的小世界拓扑结构。选取16个抑郁症患者和14个正常被试进行正负情绪面孔搜索的认知任务，同时记录头皮脑电。选择低gamma（30～50 Hz）和高gamma（50-80 Hz）两个频段的数据建立相位同步矩阵。结果显示：抑郁症患者组的全局相位同步值高于正常组；低gamma频段网络成本区间在（0．25～0．50）内，高gamma频段网络成本区间为（0．25～0．55），情绪加工网络的全局和平均局部效率介于随机网络和规则网络之间，具有小世界特性；在高gamma频段，在最大效费差下（网络成本为0．3），全局效率值在组间有显著性差异，此时局部效率无显著性差异发现；在网络成本为0．5时，发现抑郁症组在左顶颞区和枕区的局部效率显著高于正常组，在情绪间未发现显著性差异。综上反映了抑郁症患者情绪加工网络的异常特性，为抑郁症患者神经网络机制的研究提供了一定的参考。
The present study investigated whether functional brain networks are abnormally organized in the depression under the emotional stimuli. We applied phase synchronization to construct the connectivity and global and local efficiency to explore the small-world topology of the brain networks. Sixteen depressed patients and fourteen healthy subjects participated in the study, with the continuous scalp electroencephalography （EEG） collected. We constructed the phase synchronization matrices using the EEG in low gamma （30 ~ 50 Hz） and high gamma （50 ~ 80 Hz） bands. We found the global phase synchronization values of depressed group are significantly higher than healthy controls. When costs were between 0.25 and 0.50 in low gamma band and between 0.25 and 0.55 in high gamma band, global and local efficiency of brain networks were between random graph and lattice, indicating the small-world networks. When cost-efficiency reached maximum （cost equaled to 0.3 ） in high gamma band, global efficiency had significant differences between groups, without findings on local efficiency. When cost equaled to 0.5, abnormal activities were found in the left temporal-parietal and occipital region. Results reflected the abnormal network features in depression under emotional processing by the measures of global and local efficiency and provided a reference for researches on neural networks in depression.
出处
《中国生物医学工程学报》
CAS
CSCD
北大核心
2014年第5期556-563,共8页
Chinese Journal of Biomedical Engineering
基金
国家自然科学基金(61171032)
上海市教委自然科学创新重点项目(12ZZ099)
关键词
抑郁症
脑电
小世界网络
效率
depression
EEG
small-world network
efficiency
