Predicting depression based on dynamic regional connectivity: A windowed Granger causality analysis of MEG recordings
Predicting depression based on dynamic regional connectivity: A windowed Granger causality analysis of MEG recordings
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DOI：
10.1016/j.brainres.2013.08.033
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发表时间：
2013-10
期刊：
影响因子：
2.9
通讯作者：
Q. Lu;K. Bi;Chu Liu;Guoping Luo;Hao Tang;Z. Yao
中科院分区：
文献类型：
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作者：
Q. Lu;K. Bi;Chu Liu;Guoping Luo;Hao Tang;Z. Yao
可以绘制异常区域间因果关系图，以便对各种疾病进行客观诊断。这些区域间的连通性通常在整个扫描过程中计算，并用于表征连接的固定强度。然而，网络内的连通性在扫描期间可能经历实质性改变。在这项研究中，我们开发了一个客观的抑郁症识别方法，使用动态区域的相互作用，发生在响应悲伤的面部刺激。将来自视觉皮层、杏仁核、前扣带回和额下回的全时程脑磁图信号分离成连续的时间间隔。使用格兰杰因果关系图方法确定每个时间间隔内的成对相互作用模式。然后在最小冗余-最大相关性（mRMR）框架内进行特征选择。典型分类器被用来预测那些有抑郁症的患者。这些分类器的总体性能相似，最高分类准确率为87.5%。当特征的数量在一个稳健的范围内时，获得了最佳的判别性能。通过支持向量机（SVM）分析获得的判别网络模式显示异常的因果联系，涉及杏仁核在早期和晚期。杏仁核的这些早期和晚期连接似乎揭示了抑郁症患者对粗表达信息处理的负性偏见和异常的负性调制，这可能严重影响抑郁症的识别。
Abnormal inter-regional causalities can be mapped for the objective diagnosis of various diseases. These inter-regional connectivities are usually calculated over an entire scan and used to characterize the stationary strength of the connections. However, the connectivity within networks may undergo substantial changes during a scan. In this study, we developed an objective depression recognition approach using the dynamic regional interactions that occur in response to sad facial stimuli. The whole time-period magnetoencephalography (MEG) signals from the visual cortex, amygdala, anterior cingulate cortex (ACC) and inferior frontal gyrus (IFG) were separated into sequential time intervals. The Granger causality mapping method was used to identify the pairwise interaction pattern within each time interval. Feature selection was then undertaken within a minimum redundancy-maximum relevance (mRMR) framework. Typical classifiers were utilized to predict those patients who had depression. The overall performances of these classifiers were similar, and the highest classification accuracy rate was 87.5%. The best discriminative performance was obtained when the number of features was within a robust range. The discriminative network pattern obtained through support vector machine (SVM) analyses displayed abnormal causal connectivities that involved the amygdala during the early and late stages. These early and late connections in the amygdala appear to reveal a negative bias to coarse expression information processing and abnormal negative modulation in patients with depression, which may critically affect depression discrimination.