Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China; Beijing Institute for Brain Disorders, Capital Medical University, China.
Comput Methods Programs Biomed. 2018 Oct;164:169-179. doi: 10.1016/j.cmpb.2018.07.003. Epub 2018 Jul 17.
Strands of evidence have supported existence of negative attentional bias in patients with depression. This study aimed to assess the behavioral and electrophysiological signatures of attentional bias in major depressive disorder (MDD) and explore whether ERP components contain valuable information for discriminating between MDD patients and healthy controls (HCs).
Electroencephalography data were collected from 17 patients with MDD and 17 HCs in a dot-probe task, with emotional-neutral pairs as experimental materials. Fourteen features related to ERP waveform shape were generated. Then, Correlated Feature Selection (CFS), ReliefF and GainRatio (GR) were applied for feature selection. For discriminating between MDDs and HCs, k-nearest neighbor (KNN), C4.5, Sequential Minimal Optimization (SMO) and Logistic Regression (LR) were used.
Behaviorally, MDD patients showed significantly shorter reaction time (RT) to valid than invalid sad trials, with significantly higher bias score for sad-neutral pairs. Analysis of split-half reliability in RT indices indicated a strong reliability in RT, while coefficients of RT bias scores neared zero. These behavioral effects were supported by ERP results. MDD patients had higher P300 amplitude with the probe replacing a sad face than a neutral face, indicating difficult attention disengagement from negative emotional faces. Meanwhile, data mining analysis based on ERP components suggested that CFS was the best feature selection algorithm. Especially for the P300 induced by valid sad trials, the classification accuracy of CFS combination with any classifier was above 85%, and the KNN (k = 3) classifier achieved the highest accuracy (94%).
MDD patients show difficulty in attention disengagement from negative stimuli, reflected by P300. The CFS over other methods leads to a good overall performance in most cases, especially when KNN classifier is used for P300 component classification, illustrating that ERP component may be applied as a tool for auxiliary diagnosis of depression.
有大量证据表明，抑郁症患者存在负性注意偏向。本研究旨在评估重性抑郁障碍（MDD）患者注意偏向的行为和电生理特征，并探讨 ERP 成分是否包含有助于区分 MDD 患者和健康对照（HC）的信息。
采用点探测任务，以情绪性-中性对作为实验材料，对 17 例 MDD 患者和 17 例 HC 进行脑电数据采集。生成与 ERP 波形形状相关的 14 个特征。然后，应用相关特征选择（CFS）、 ReliefF 和增益比（GR）进行特征选择。采用 K 近邻（KNN）、C4.5、序贯最小优化（SMO）和逻辑回归（LR）对 MDD 和 HC 进行区分。
行为学上，MDD 患者对有效（sad）-无效（neutral）悲伤试次的反应时（RT）明显缩短，对悲伤-中性对的偏向得分明显更高。对 RT 指标的分半信度分析表明，RT 具有较强的可靠性，而 RT 偏向得分的系数接近零。这些行为学效应得到了 ERP 结果的支持。与中性面孔相比，当探针取代悲伤面孔时，MDD 患者的 P300 振幅更高，表明他们难以将注意力从负性情绪面孔上转移开。同时，基于 ERP 成分的数据挖掘分析表明，CFS 是最佳的特征选择算法。特别是对于有效悲伤试次诱发的 P300，CFS 与任何分类器组合的分类准确率均高于 85%，其中 KNN（k=3）分类器的准确率最高（94%）。
MDD 患者在注意力从负性刺激上转移时存在困难，这反映在 P300 上。与其他方法相比，CFS 方法在大多数情况下表现良好，尤其是当 KNN 分类器用于 P300 成分分类时，这表明 ERP 成分可能作为抑郁症辅助诊断的工具。
