Bi Kun, Chattun Mohammad Ridwan, Liu Xiaoxue, Wang Qiang, Tian Shui, Zhang Siqi, Lu Qing, Yao Zhijian
School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing 210096, China.
Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China.
J Affect Disord. 2018 Oct 1;238:366-374. doi: 10.1016/j.jad.2018.05.078. Epub 2018 Jun 15.
The functional networks are associated with emotional processing in depression. The mapping of dynamic spatio-temporal brain networks is used to explore individual performance during early negative emotional processing. However, the dysfunctions of functional networks in low gamma band and their discriminative potentialities during early period of emotional face processing remain to be explored.
Functional brain networks were constructed from the MEG recordings of 54 depressed patients and 54 controls in low gamma band (30-48 Hz). Dynamic connectivity regression (DCR) algorithm analyzed the individual change points of time series in response to emotional stimuli and constructed individualized spatio-temporal patterns. The nodal characteristics of patterns were calculated and fed into support vector machine (SVM). Performance of the classification algorithm in low gamma band was validated by dynamic topological characteristics of individual patterns in comparison to alpha and beta band.
The best discrimination accuracy of individual spatio-temporal patterns was 91.01% in low gamma band. Individual temporal patterns had better results compared to group-averaged temporal patterns in all bands. The most important discriminative networks included affective network (AN) and fronto-parietal network (FPN) in low gamma band.
The sample size is relatively small. High gamma band was not considered.
The abnormal dynamic functional networks in low gamma band during early emotion processing enabled depression recognition. The individual information processing is crucial in the discovery of abnormal spatio-temporal patterns in depression during early negative emotional processing. Individual spatio-temporal patterns may reflect the real dynamic function of subjects while group-averaged data may neglect some individual information.
功能网络与抑郁症中的情绪处理相关。动态时空脑网络映射用于探索早期负性情绪处理过程中的个体表现。然而，低伽马波段功能网络的功能障碍及其在情绪面孔处理早期的辨别潜力仍有待探索。
从54名抑郁症患者和54名对照的脑磁图记录中构建低伽马波段（30 - 48赫兹）的功能脑网络。动态连接性回归（DCR）算法分析了时间序列响应情绪刺激的个体变化点，并构建了个体化的时空模式。计算模式的节点特征并将其输入支持向量机（SVM）。与阿尔法和贝塔波段相比，通过个体模式的动态拓扑特征验证了低伽马波段分类算法的性能。
低伽马波段个体时空模式的最佳辨别准确率为91.01%。在所有波段中，个体时间模式比组平均时间模式有更好的结果。最重要的辨别网络在低伽马波段包括情感网络（AN）和额顶网络（FPN）。
样本量相对较小。未考虑高伽马波段。
早期情绪处理过程中低伽马波段异常的动态功能网络有助于识别抑郁症。个体信息处理在发现早期负性情绪处理过程中抑郁症异常时空模式方面至关重要。个体时空模式可能反映受试者的真实动态功能，而组平均数据可能忽略一些个体信息。
