Xiaowei Li1, Jianxiu Li2, Bin Hu3, Jing Zhu4, Xuemin Zhang5, Liuqing Wei6, Ning Zhong7, Mi Li8, Zhijie Ding9, Jing Yang10, Lan Zhang10. 1. Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China. Electronic address: lixwei@lzu.edu.cn. 2. Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China. Electronic address: jxli2015@lzu.edu.cn. 3. 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. Electronic address: bh@lzu.edu.cn. 4. Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China. Electronic address: zhujing@lzu.edu.cn. 5. Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China. Electronic address: xmzhang@bnu.edu.cn. 6. Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China. 7. International WIC Institute, Beijing University of Technology, Beijing, China. Electronic address: zhong@maebashi-it.ac.jp. 8. International WIC Institute, Beijing University of Technology, Beijing, China. Electronic address: limi@bjut.edu.cn. 9. The Third People's Hospital of Tianshui City, Tianshui, China. Electronic address: 407493919@qq.com. 10. Department of Child Psychology, Lanzhou University Second Hospital, Lanzhou, China.
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.
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 MDDpatients 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, MDDpatients 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. MDDpatients 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:MDDpatients 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.