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Chen W, Cai Y, Li A, Jiang K, Su Y. MDD brain network analysis based on EEG functional connectivity and graph theory. Heliyon 2024; 10:e36991. [PMID: 39281492 PMCID: PMC11402240 DOI: 10.1016/j.heliyon.2024.e36991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 08/26/2024] [Accepted: 08/26/2024] [Indexed: 09/18/2024] Open
Abstract
Background Existing studies have shown that the brain network of major depression disorder (MDD) has abnormal topologies. However, constructing reliable MDD brain networks is still an open problem. New method This paper proposed a reliable MDD brain network construction method. First, seven connectivity methods are used to calculate the correlation between channels and obtain the functional connectivity matrix. Then, the matrix is binarized using four binarization methods to obtain the EEG brain network. Besides, we proposed an improved binarization method based on the criterion of maximizing differences between groups: the adaptive threshold (AT) method. The AT can automatically set the optimal binarization threshold and overcome the artificial influence of traditional methods. After that, several network metrics are extracted from the brain network to analyze inter-group differences. Finally, we used statistical analysis and Fscore values to compare the performance of different methods and establish the most reliable method for brain network construction. Results In theta, alpha, and total frequency bands, the clustering coefficient, global efficiency, local efficiency, and degree of the MDD brain network decrease, and the path length of the MDD brain network increases. Comparison with existing methods The results show that AT outperforms the existing binarization methods. Compared with other methods, the brain network construction method based on phase-locked value (PLV) and AT has better reliability. Conclusions MDD has brain dysfunction, particularly in the frontal and temporal lobes.
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Affiliation(s)
- Wan Chen
- Rocket Force University of Engineering, Xi'an, 710025, China
| | - Yanping Cai
- Rocket Force University of Engineering, Xi'an, 710025, China
| | - Aihua Li
- Rocket Force University of Engineering, Xi'an, 710025, China
| | - Ke Jiang
- Rocket Force University of Engineering, Xi'an, 710025, China
| | - Yanzhao Su
- Rocket Force University of Engineering, Xi'an, 710025, China
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Zhao Z, Ran X, Wang J, Lv S, Qiu M, Niu Y, Wang C, Xu Y, Gao Z, Ren W, Zhou X, Fan X, Song J, Yu Y. Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatment. J Affect Disord 2024; 367:777-787. [PMID: 39265862 DOI: 10.1016/j.jad.2024.09.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Revised: 08/31/2024] [Accepted: 09/08/2024] [Indexed: 09/14/2024]
Abstract
OBJECTIVE Repetitive transcranial magnetic stimulation (rTMS) has recently emerged as a novel treatment option for patients with major depressive disorder (MDD), but clinical observations reveal variability in patient's responses to rTMS. Therefore, it is clinically significant to investigate the baseline neuroimaging differences between patients with (Responder) and without (NonResponder) response to rTMS treatment and predict rTMS treatment outcomes based on baseline neuroimaging data. METHOD Baseline resting-state EEG data and Beck Depression Inventory (BDI) were collected from 74 rTMS Responder, 43 NonResponder, and 47 matched healthy controls (HC). EEG microstate analysis was applied to analyze common and differential microstate characteristics of Responder and NonResponder. In addition, the microstate temporal parameters were sent to four machine learning models to classify Responder from NonResponder. RESULT There exists some common and differential EEG microstate characteristics for Responder and NonResponder. Specifically, compared to the HC group, both Responder and NonResponder exhibited a significant increase in the occurrence of microstate A. Only Responder showed an increase in the coverage of microstate A, occurrence of microstate D, transition probability (TP) from A to D, D to A, and C to A, and a decrease in the duration of microstates B and E, TP from A to B and C to B compared to HC. Only NonResponder exhibited a significant decrease in the duration of microstate D, TP from C to D, and an increase in the occurrence of microstate E, TP from C to E compared to HC. The primary differences between the Responder and NonResponder are that Responder had higher parameters for microstate D, TP from other microstates to D, and lower parameters for microstate E, TP from other microstates to E compared to NonResponder. Baseline parameters of microstate D showed significant correlation with Beck Depression Inventory (BDI) reduction rate. Additionally, these microstate features were sent to four machine learning models to predict rTMS treatment response and classification results indicate that an excellent predicting performance (accuracy = 97.35 %, precision = 96.31 %, recall = 100 %, F1 score = 98.06 %) was obtained when using AdaBoost model. These results suggest that baseline resting-state EEG microstate parameters could serve as robust indicators for predicting the effectiveness of rTMS treatment. CONCLUSION This study reveals significant baseline EEG microstate differences between rTMS Responder, NonResponder, and healthy controls. Microstates D and E in baseline EEG can serve as potential biomarkers for predicting rTMS treatment outcomes in MDD patients. These findings may aid in identifying patients likely to respond to rTMS, optimizing treatment plans and reducing trial-and-error approaches in therapy selection.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; The Second Affiliated Hospital of Xinxiang Medical University, Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China; Henan Engineering Research Center of Physical Diagnostics and Treatment Technology for the Mental and Neurological Diseases, People's Republic of China.
| | - Xiangying Ran
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Junming Wang
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Shiyang Lv
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Mengyue Qiu
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Yanxiang Niu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, People's Republic of China
| | - Chang Wang
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Yongtao Xu
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Zhixian Gao
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Wu Ren
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Xuezhi Zhou
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Xiaofeng Fan
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Jinggui Song
- Henan Engineering Research Center of Physical Diagnostics and Treatment Technology for the Mental and Neurological Diseases, People's Republic of China
| | - Yi Yu
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China.
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Wang Y, Peng Y, Han M, Liu X, Niu H, Cheng J, Chang S, Liu T. GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals. J Neural Eng 2024; 21:036042. [PMID: 38788706 DOI: 10.1088/1741-2552/ad5048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 05/24/2024] [Indexed: 05/26/2024]
Abstract
Objective.Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Approach.Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance.Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls, in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks.Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.
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Affiliation(s)
- Yuwen Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Yudan Peng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Mingxiu Han
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Xinyi Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, People's Republic of China
| | - Suhua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, People's Republic of China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
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Zhang B, Wei D, Yan G, Li X, Su Y, Cai H. Spatial-Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection. Interdiscip Sci 2023; 15:542-559. [PMID: 37140772 PMCID: PMC10158716 DOI: 10.1007/s12539-023-00567-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 05/05/2023]
Abstract
In view of the major depressive disorder characteristics such as high mortality as well as high recurrence, it is important to explore an objective and effective detection method for major depressive disorder. Considering the advantages complementary of different machine learning algorithms in information mining process, as well as the fusion complementary of different information, in this study, the spatial-temporal electroencephalography fusion framework using neural network is proposed for major depressive disorder detection. Since electroencephalography is a typical time series signal, we introduce recurrent neural network embedded in long short-term memory unit for extract temporal domain features to solve the problem of long-distance information dependence. To reduce the volume conductor effect, the temporal electroencephalography data are mapping into a spatial brain functional network using phase lag index, then the spatial domain features were extracted from brain functional network using 2D convolutional neural networks. Considering the complementarity between different types of features, the spatial-temporal electroencephalography features are fused to achieve data diversity. The experimental results show that spatial-temporal features fusion can improve the detection accuracy of major depressive disorder with a highest of 96.33%. In addition, our research also found that theta, alpha, and full frequency band in brain regions of left frontal, left central, right temporal are closely related to MDD detection, especially theta frequency band in left frontal region. Only using single-dimension EEG data as decision basis, it is difficult to fully explore the valuable information hidden in the data, which affects the overall detection performance of MDD. Meanwhile, different algorithms have their own advantages for different application scenarios. Ideally, different algorithms should use their respective advantages to jointly address complex problems in engineering fields. To this end, we propose a computer-aided MDD detection framework based on spatial-temporal EEG fusion using neural network, as shown in Fig. 1. The simplified process is as follows: (1) Raw EEG data acquisition and preprocessing. (2) The time series EEG data of each channel are input as recurrent neural network (RNN), and RNN is used to process and extract temporal domain (TD) features. (3) The BFN among different EEG channels is constructed, and CNN is used to process and extract the spatial domain (SD) features of the BFN. (4) Based on the theory of information complementarity, the spatial-temporal information is fused to realize efficient MDD detection. Fig. 1 MDD detection framework based on spatial-temporal EEG fusion.
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Affiliation(s)
- Bingtao Zhang
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China.
- Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University, Lanzhou, 730070, China.
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China.
| | - Dan Wei
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
| | - Guanghui Yan
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
| | - Xiulan Li
- Gansu Province Big Data Center, Lanzhou, 730000, China.
| | - Yun Su
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
| | - Hanshu Cai
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
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Ma Y, Qian J, Gu Q, Yi W, Yan W, Yuan J, Wang J. Network Analysis of Depression Using Magnetoencephalogram Based on Polynomial Kernel Granger Causality. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1330. [PMID: 37761629 PMCID: PMC10529343 DOI: 10.3390/e25091330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/09/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
Depression is a psychiatric disorder characterized by anxiety, pessimism, and suicidal tendencies, which has serious impact on human's life. In this paper, we use Granger causality index based on polynomial kernel as network node connectivity coefficient to construct brain networks from the magnetoencephalogram (MEG) of 5 depressed patients and 11 healthy individuals under positive, neutral, and negative emotional stimuli, respectively. We found that depressed patients had more information exchange between the frontal and occipital regions compared to healthy individuals and less causal connections in the parietal and central regions. We further analyzed the topological properties of the network revealed and found that depressed patients had higher average degrees under negative stimuli (p = 0.008) and lower average clustering coefficients than healthy individuals (p = 0.034). When comparing the average degree and average clustering coefficient of the same sample under different emotional stimuli, we found that depressed patients had a higher average degree and average clustering coefficient under negative stimuli than neutral and positive stimuli. We also found that the characteristic path lengths of patients under negative and neutral stimuli significantly deviated from small-world network. Our results suggest that the analysis of polynomial kernel Granger causality brain networks can effectively characterize the pathology of depression.
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Affiliation(s)
- Yijia Ma
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jing Qian
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Qizhang Gu
- School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Wanyi Yi
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Wei Yan
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Jianxuan Yuan
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jun Wang
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Jiang W, Ding S, Xu C, Ke H, Bo H, Zhao T, Ma L, Li H. Discovering the neuronal dynamics in major depressive disorder using Hidden Markov Model. Front Hum Neurosci 2023; 17:1197613. [PMID: 37457501 PMCID: PMC10340116 DOI: 10.3389/fnhum.2023.1197613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction Major Depressive Disorder (MDD) is a leading cause of worldwide disability, and standard clinical treatments have limitations due to the absence of neurological evidence. Electroencephalography (EEG) monitoring is an effective method for recording neural activities and can provide electroneurophysiological evidence of MDD. Methods In this work, we proposed a probabilistic graphical model for neural dynamics decoding on MDD patients and healthy controls (HC), utilizing the Hidden Markov Model with Multivariate Autoregressive observation (HMM-MAR). We testified the model on the MODMA dataset, which contains resting-state and task-state EEG data from 53 participants, including 24 individuals with MDD and 29 HC. Results The experimental results suggest that the state time courses generated by the proposed model could regress the Patient Health Questionnaire-9 (PHQ-9) score of the participants and reveal differences between the MDD and HC groups. Meanwhile, the Markov property was observed in the neuronal dynamics of participants presented with sad face stimuli. Coherence analysis and power spectrum estimation demonstrate consistent results with the previous studies on MDD. Discussion In conclusion, the proposed HMM-MAR model has revealed its potential capability to capture the neuronal dynamics from EEG signals and interpret brain disease pathogenesis from the perspective of state transition. Compared with the previous machine-learning or deep-learning-based studies, which regarded the decoding model as a black box, this work has its superiority in the spatiotemporal pattern interpretability by utilizing the Hidden Markov Model.
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Affiliation(s)
- Wenhao Jiang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Shihang Ding
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Cong Xu
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Huihuang Ke
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Hongjian Bo
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
| | - Tiejun Zhao
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Lin Ma
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Haifeng Li
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
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Yang J, Zhang Z, Fu Z, Li B, Xiong P, Liu X. Cross-subject classification of depression by using multiparadigm EEG feature fusion. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107360. [PMID: 36944276 DOI: 10.1016/j.cmpb.2023.107360] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 12/22/2022] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE The aim of this study is to solve the non-stationarity and complexity characteristics and huge individual differences in the electroencephalogram (EEG) signals for depression classification. METHODS To address those problems, the Lempel-Ziv complexity feature matrices were extracted from the EEG signals under the two paradigms of eyes open and eyes closed in the resting state. Topographical map of brain and statistical analysis were introduced to investigate the significance of eyes open and eyes closed EEG for depression classification. To promote the classification accuracy, feature matrices from the two paradigms were fused. And linear combination and concatenation fusion methods were proposed to further reveal the underlying mechanism of improving classification accuracy. Support vector machine (SVM), K-nearest neighbor, and decision tree classifiers were employed and compared to classify depression under the eyes open, eyes closed and fused paradigm. RESULTS The classification results of 10-fold cross-validation showed that the highest average accuracy (86.58%) under a single paradigm was achieved in the eyes-open state. The multiparadigm fusion method of concatenation was better than the linear combination. The best classification result was obtained using multiparadigm feature concatenation under the SVM classifier, yielding an accuracy of 94.03%. CONCLUSION The multiparadigm feature fusion method proposed in this paper can effectively improve the accuracy of depression classification. It was proved that eyes open and eyes closed EEG have complementary information, which was benefit for the cross-subject classification of depression. It provides new ideas for depression classification in clinics.
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Affiliation(s)
- Jianli Yang
- College of Electronic Information and Engineering, Hebei University, Baoding 071002, China; Key Laboratory of Digital Medical Engineering of Heibei Province, Baoding 071002, China
| | - Zhen Zhang
- College of Electronic Information and Engineering, Hebei University, Baoding 071002, China
| | - Zhiyu Fu
- College of Electronic Information and Engineering, Hebei University, Baoding 071002, China
| | - Bing Li
- Hebei Mental Health Center, Baoding 071000, China; Hebei Key Laboratory of Mental and Behavioral Disorders Research, Baoding 071000, China
| | - Peng Xiong
- College of Electronic Information and Engineering, Hebei University, Baoding 071002, China; Key Laboratory of Digital Medical Engineering of Heibei Province, Baoding 071002, China.
| | - Xiuling Liu
- College of Electronic Information and Engineering, Hebei University, Baoding 071002, China; Key Laboratory of Digital Medical Engineering of Heibei Province, Baoding 071002, China.
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Yang L, Wang Y, Zhu X, Yang X, Zheng C. A gated temporal-separable attention network for EEG-based depression recognition. Comput Biol Med 2023; 157:106782. [PMID: 36931203 DOI: 10.1016/j.compbiomed.2023.106782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/14/2023] [Accepted: 03/09/2023] [Indexed: 03/13/2023]
Abstract
Depression, a common mental illness worldwide, needs to be diagnosed and cured at an early stage. To assist clinical diagnosis, an EEG-based deep learning frame, which is named the gated temporal-separable attention network (GTSAN), is proposed in this paper for depression recognition. GTSAN model extracts discriminative information from EEG recordings in two ways. On the one hand, the gated recurrent unit (GRU) is used in the GTSAN model to capture the EEG historical information to form the features. On the other hand, the model digs the multilevel information by using an improved version of temporal convolutional network (TCN), called temporal-separable convolution network (TSCN), which applies causal convolution and dilated convolution to extract features from fine to coarse scales. The TSCN and GRU features can be produced in parallel. Finally, the new model introduces the attention mechanism to give different weights to these features, allowing them to be used to identify depression more effectively. Experiments on two depression datasets have demonstrated that the proposed model can mine potential depression patterns in data and obtain high recognition accuracies. The proposed model provides the possibility of using an EEG-based system to assist for diagnosing depression.
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Affiliation(s)
- Lijun Yang
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China; Center for Applied Mathematics of Henan Province, Henan University, Zhengzhou, 450046, China.
| | - Yixin Wang
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China.
| | - Xiangru Zhu
- Institute of Cognition, Brain, and Health, Henan University, Kaifeng 475004, China.
| | - Xiaohui Yang
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China; Center for Applied Mathematics of Henan Province, Henan University, Zhengzhou, 450046, China.
| | - Chen Zheng
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China; Center for Applied Mathematics of Henan Province, Henan University, Zhengzhou, 450046, China.
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Shao X, Kong W, Sun S, Li N, Li X, Hu B. Analysis of functional connectivity in depression based on a weighted hyper-network method. J Neural Eng 2023; 20. [PMID: 36603214 DOI: 10.1088/1741-2552/acb088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 01/05/2023] [Indexed: 01/06/2023]
Abstract
Objective. Brain connectivity network is a vital tool to reveal the interaction between different brain regions. Currently, most functional connectivity methods can only capture pairs of information to construct brain networks which ignored the high-order correlations between brain regions.Approach. Therefore, this study proposed a weighted connectivity hyper-network based on resting-state EEG data, and then applied to depression identification and analysis. The hyper-network model was build based on least absolute shrinkage and selection operator sparse regression method to effectively represent the higher-order relationships of brain regions. On this basis, by integrating the correlation-based weighted hyper-edge information, the weighted hyper-network is constructed, and the topological features of the network are extracted for classification.Main results. The experimental results obtained an optimal accuracy compared to the traditional coupling methods. The statistical results on network metrics proved that there were significant differences between depressive patients and normal controls. In addition, some brain regions and electrodes were found and discussed to highly correlate with depression by analyzing of the critical nodes and hyper-edges.Significance. These may help discover disease-related biomarkers important for depression diagnosis.
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Affiliation(s)
- Xuexiao Shao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Wenwen Kong
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Shuting Sun
- Brain Health Engineering Laboratory, Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Na Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.,Shandong Academy of Intelligent Computing Technology, Shandong, People's Republic of China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.,Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences; Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, People's Republic of China
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10
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Venkatapathy S, Votinov M, Wagels L, Kim S, Lee M, Habel U, Ra IH, Jo HG. Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity. Front Psychiatry 2023; 14:1125339. [PMID: 37032921 PMCID: PMC10077869 DOI: 10.3389/fpsyt.2023.1125339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/02/2023] [Indexed: 04/11/2023] Open
Abstract
Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.
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Affiliation(s)
- Sujitha Venkatapathy
- School of Computer Information and Communication Engineering, Kunsan National University, Gunsan, Republic of Korea
| | - Mikhail Votinov
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
- Research Center Juelich, Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Juelich, Republic of Korea
| | - Lisa Wagels
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
- Research Center Juelich, Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Juelich, Republic of Korea
| | - Sangyun Kim
- AI Convergence Research Section, Electronics and Telecommunications Research Institute, Gwangju, Republic of Korea
| | - Munseob Lee
- AI Convergence Research Section, Electronics and Telecommunications Research Institute, Gwangju, Republic of Korea
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, Uniklinik RWTH Aachen University, Aachen, Germany
- Research Center Juelich, Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Juelich, Republic of Korea
| | - In-Ho Ra
- School of Computer Information and Communication Engineering, Kunsan National University, Gunsan, Republic of Korea
| | - Han-Gue Jo
- School of Computer Information and Communication Engineering, Kunsan National University, Gunsan, Republic of Korea
- *Correspondence: Han-Gue Jo
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11
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He S, Li Y, Le X, Han X, Lin J, Peng X, Li M, Yang R, Yao D, Valdes-Sosa PA, Ren P. Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2023; 31:1764-1775. [PMID: 37030736 DOI: 10.1109/tnsre.2023.3260143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
OBJECTIVE Multivariate signal (MS) analysis, especially the assessment of its information transmission (for example, from the perspective of network science), is the key to our understanding of various phenomena in biology, physics and economics. Although there is a large amount of literature demonstrating that MS can be decomposed into space-time-frequency domain information, there seems to be no research confirming that multivariate information transmission (MIT) in these three domains can be quantified. Therefore, in this study, we attempted to combine dynamic mode decomposition (DMD) and parallel communication model (PCM) together to realize it. METHODS We first regarded MS as a large-scale system and then used DMD to decompose it into specific subsystems with their own intrinsic oscillatory frequencies. At the same time, the transition probability matrix (TPM) of information transmission within and between MS at two consecutive moments in each subsystem can also be calculated. Then, communication parameters (CPs) derived from each TPM were calculated in order to quantify the MIT in the space-time-frequency domain. In this study, multidimensional electroencephalogram (EEG) signals were used to illustrate our method. RESULTS Compared with traditional EEG brain networks, this method shows greater potential in EEG analysis to distinguish between patients and healthy controls. CONCLUSION This study demonstrates the feasibility of measuring MIT in the space-time-frequency domain simultaneously. SIGNIFICANCE This study shows that MIT analysis in the space-time-frequency domain is not only completely different from the MS decomposition in these three domains, but also can reveal many new phenomena behind MS that have not yet been discovered.
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12
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Altered Functional Connectivity and Complexity in Major Depressive Disorder after Musical Stimulation. Brain Sci 2022; 12:brainsci12121680. [PMID: 36552139 PMCID: PMC9775252 DOI: 10.3390/brainsci12121680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/24/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
Major depressive disorder (MDD) is a common mental illness. This study used electroencephalography (EEG) to explore the effects of music therapy on brain networks in MDD patients and to elucidate changes in functional brain connectivity in subjects before and after musical stimulation. EEG signals were collected from eight MDD patients and eight healthy controls. The phase locking value was adopted to calculate the EEG correlation of different channels in different frequency bands. Correlation matrices and network topologies were studied to analyze changes in functional connectivity between brain regions. The results of the experimental analysis found that the connectivity of the delta and beta bands decreased, while the connectivity of the alpha band increased. Regarding the characteristics of the EEG functional network, the average clustering coefficient, characteristic path length and degree of each node in the delta band decreased significantly after musical stimulation, while the characteristic path length in the beta band increased significantly. Characterized by the average clustering coefficient and characteristic path length, the classification of depression and healthy controls reached 93.75% using a support vector machine.
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13
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Teng C, Wang M, Wang W, Ma J, Jia M, Wu M, Luo Y, Wang Y, Zhang Y, Xu J. Abnormal Properties of Cortical Functional Brain Network in Major Depressive Disorder: Graph Theory Analysis Based on Electroencephalography-Source Estimates. Neuroscience 2022; 506:80-90. [PMID: 36272697 DOI: 10.1016/j.neuroscience.2022.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/04/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022]
Abstract
Studies of scalp electroencephalography (EEG) had shown altered topological organization of functional brain networks in patients with major depressive disorder (MDD). However, most previous EEG-based network analyses were performed at sensor level, while the interpretation of obtained results was not straightforward due to volume conduction effect. To reduce the impact of this defect, the whole cortical functional brain networks of MDD patients were studied during resting state based on EEG-source estimates in this paper. First, scalp EEG signals were recorded from 19 patients with MDD and 20 normal controls under resting eyes-closed state, and cortical neural signals were estimated by using sLORETA method. Then, the correntropy coefficient of wavelet packet coefficients was performed to calculate functional connectivity (FC) matrices in four different frequency bands: δ, θ, α, β, respectively. Afterwards, topological properties of brain networks were analyzed by graph theory approaches. The results showed that the global FC strength of MDD patients was significantly higher than that of healthy subjects in α band. Also, it was found that MDD patients have abnormally increased clustering coefficient and local efficiency in both α and β bands compared to normal people. Furthermore, patients with MDD exhibited increased nodal clustering coefficients in the left lingual gryus and left precuneus in α band. In addition, β band global clustering coefficient was positively correlated with the scores of depression severity. Therefore, the findings indicated the cortical functional brain networks in MDD patients were disruptions, which suggested it would be one of potential causes of depression.
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Affiliation(s)
- Chaolin Teng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China; Department of Aerospace Medicine, The Air Force Medical University, Xi'an, Shaanxi 710068, PR China
| | - Mengwei Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Wei Wang
- Department of Psychiatry, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, PR China
| | - Jin Ma
- Department of Aerospace Medicine, The Air Force Medical University, Xi'an, Shaanxi 710068, PR China
| | - Min Jia
- Department of Psychiatry, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, PR China
| | - Min Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Yuanyuan Luo
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China; Department of Psychology, Xi'an Mental Health Center, Xi'an, Shaanxi 710061, PR China
| | - Yu Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Yiyang Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Jin Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China.
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14
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Zhang B, Wei D, Yan G, Lei T, Cai H, Yang Z. Feature-level fusion based on spatial-temporal of pervasive EEG for depression recognition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107113. [PMID: 36103735 DOI: 10.1016/j.cmpb.2022.107113] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/23/2022] [Accepted: 09/04/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE In view of the depression characteristics such as high prevalence, high disability rate, high fatality rate, and high recurrence rate, early identification and early intervention are the most effective methods to prevent irreversible damage of brain function over time. The traditional method of depression recognition based on questionnaires and interviews is time-consuming and labor-intensive, and heavily depends on the doctor's subjective experience. Therefore, accurate, convenient and effective recognition of depression has important social value and scientific significance. METHODS This paper proposes a depression recognition framework based on feature-level fusion of spatial-temporal pervasive electroencephalography (EEG). Time series EEG data were collected by portable three-electrode EEG acquisition instrument, and mapped to a spatial complex network called visibility graph (VG). Then temporal EEG features and spatial VG metric features were extracted and selected. Based on the correlation between features and categories, the differences in contribution of individual feature are explored, and different contribution coefficients are assigned to different features as the data basis of feature-level fusion to ensure the diversity of data. A cascade forest model based on three different decision forests is designed to realize the efficient depression recognition using spatial-temporal feature-level fusion data. RESULTS Experimental data were obtained from 26 depressed patients and 29 healthy controls (HC). The results of multiple control experiments show that compared with single type feature, feature-level fusion without contribution coefficient, and independent classifiers, the feature-level method with contribution coefficient of spatial-temporal has a stronger recognition ability of depression, and the highest accuracy is 92.48%. CONCLUSION Feature-level fusion method provides an effective computer-aided tool for rapid clinical diagnosis of depression.
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Affiliation(s)
- Bingtao Zhang
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
| | - Dan Wei
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
| | - Guanghui Yan
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
| | - Tao Lei
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Haishu Cai
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Zhifei Yang
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
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15
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Zhao F, Gao T, Cao Z, Chen X, Mao Y, Mao N, Ren Y. Identifying depression disorder using multi-view high-order brain function network derived from electroencephalography signal. Front Comput Neurosci 2022; 16:1046310. [PMID: 36387303 PMCID: PMC9647659 DOI: 10.3389/fncom.2022.1046310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 10/10/2022] [Indexed: 11/03/2023] Open
Abstract
Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.
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Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Tianyu Gao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Zhi Cao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yanyan Mao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
- College of Oceanography and Space Informatics, China University of Petroleum (Huadong), Qingdao, Shandong, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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16
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Zhang B, Cai H, Song Y, Tao L, Li Y. Computer-aided Recognition Based on Decision-level Multimodal Fusion for Depression. IEEE J Biomed Health Inform 2022; 26:3466-3477. [PMID: 35389872 DOI: 10.1109/jbhi.2022.3165640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Aiming at the problem of depression recognition, this paper proposes a computer-aided recognition framework based on decision-level multimodal fusion. In Song Dynasty of China, the idea of multimodal fusion was contained in "one gets different impressions of a mountain when viewing it from the front or sideways, at a close range or from afar" poetry. Objective and comprehensive analysis of depression can more accurately restore its essence, and multimodal can represent more information about depression compared to single modal. Linear electroencephalography (EEG) features based on adaptive auto regression (AR) model and typical nonlinear EEG features are extracted. EEG features related to depression and graph metric features in depression related brain regions are selected as the data basis of multimodal fusion to ensure data diversity. Based on the theory of multi-agent cooperation, the computer-aided depression recognition model of decision-level is realized. The experimental data comes from 24 depressed patients and 29 healthy controls (HC). The results of multi-group controlled trials show that compared with single modal or independent classifiers, the decision-level multimodal fusion method has a stronger ability to recognize depression, and the highest accuracy rate 92.13% was obtained. In addition, our results suggest that improving the brain region associated with information processing can help alleviate and treat depression. In the field of classification and recognition, our results clarify that there is no universal classifier suitable for any condition.
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