Attentional bias in MDD: ERP components analysis and classification using a dot-probe task. (October 2018) - Li, Xiaowei; Li, Jianxiu; Hu, Bin; Zhu, Jing; Zhang, Xuemin; Wei, Liuqing; Zhong, Ning; Li, Mi; Ding, Zhijie; Yang, Jing; Zhang, Lan
Highlights: MDDs exhibit difficulty disengaging from sad faces by enhanced P300 amplitude to valid sad trials. Based on ERP components, apply three feature selection methods and four classifiers to classify MDDs. Feature selection method CFS combine with KNN can achieve the optimal performance for MDDs detection. Abstract: Background and Objective: 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). Methods: 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. Results: 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%). Conclusions: 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.