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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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Chen M, Mohammadi M, Izadpanah S. Language learning through music on the academic achievement, creative thinking, and self-esteem of the English as a foreign language (EFL) learners. Acta Psychol (Amst) 2024; 247:104318. [PMID: 38735248 DOI: 10.1016/j.actpsy.2024.104318] [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: 11/25/2023] [Revised: 04/14/2024] [Accepted: 05/09/2024] [Indexed: 05/14/2024] Open
Abstract
This study aimed to comprehensively analyze Language Learning through Music on the academic achievement (AA), creative thinking (CT), and self-esteem (SE) of English as a Foreign Language (EFL) Learners. With the rapid progress of technology, there has been a growing interest in exploring innovative teaching methods that not only enhance learning outcomes but also actively engage students in the language learning process. However, the specific impact of technology-enhanced language learning through music (TELLTM) on these language learning outcomes has received limited attention in previous research. In 2023, a sample of 360 male elementary-level language learners was selected using a multiple-stage cluster sampling (MSCS) technique. The participants' homogeneity was assessed through the Oxford Quick Placement Test (OQPT), administered following a random sampling procedure. Data collection involved the administration of three questionnaires: The academic achievement questionnaire, the self-esteem questionnaire, and the creative thinking questionnaire. The findings of the study analyzed through descriptive and inferential statistics, revealed a significant positive impact of TELLTM on the AA, CT, and SE of EFL learners. These results have important implications for educators, curriculum developers, and policymakers, providing valuable insights into the incorporation of TELLTM into English language instruction. The use of these three questionnaires provided valuable insights into the effectiveness of TELLTM in enhancing various aspects of language learning. These findings underscore the importance of incorporating music into language instruction and offer practical guidance for educators seeking to improve their teaching practices.
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Affiliation(s)
- Meng Chen
- Department of Orchestra, Conservatory of Music, Harbin Normal University, 150080 Harbin, China
| | - Mohammad Mohammadi
- Department of English Language Teaching, Zanjan Branch, Islamic Azad University, Zanjan, Iran
| | - Siros Izadpanah
- Department of English Language Teaching, Zanjan Branch, Islamic Azad University, Zanjan, Iran.
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Yin J, Xu G, Xie H, Liu Y, Dou Z, Shao B, Li Z. Effects of different frequencies music on cortical responses and functional connectivity in patients with minimal conscious state. JOURNAL OF BIOPHOTONICS 2024; 17:e202300427. [PMID: 38303080 DOI: 10.1002/jbio.202300427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/04/2023] [Accepted: 12/19/2023] [Indexed: 02/03/2024]
Abstract
The objective of this study was to investigate brain activation and functional network patterns during musical interventions in different frequency bands using functional near-infrared spectroscopy, and to provide a basis for more effective music therapy strategy selection for patients in minimally conscious state (MCS). Twenty six MCS patients and 20 healthy people were given music intervention with low frequency (31-180 Hz), medium frequency (180-4k Hz), and high frequency (4k-22k Hz) audio. In MCS patients, low frequency music intervention induced activation of left prefrontal cortex and left primary sensory cortex (S1), also a left-hemisphere lateralization effect of dorsolateral prefrontal cortex (DLPFC). And the functional connectivity of right DLPFC-right S1 was significantly improved by high frequency music intervention. The low frequency and high frequency music may contribute more than medium frequency music to the recovery of consciousness. This study also validated the effectiveness of fNIRS in studies of brain function in MCS patients.
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Affiliation(s)
- Jiahui Yin
- School of Physical Education, Shanghai University of Sport, Shanghai, China
| | - Gongcheng Xu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hui Xie
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Ying Liu
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- Key Laboratory of Neuro-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing, China
| | - Zulin Dou
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bin Shao
- School of Physical Education, Shanghai University of Sport, Shanghai, China
| | - Zengyong Li
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- Key Laboratory of Neuro-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing, China
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Yuan EJ, Chang CH, Chen HH, Huang SS. The effects of electroencephalography functional connectivity during emotional recognition among patients with major depressive disorder and healthy controls. J Psychiatr Res 2024; 172:16-23. [PMID: 38350225 DOI: 10.1016/j.jpsychires.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 01/01/2024] [Accepted: 02/01/2024] [Indexed: 02/15/2024]
Abstract
BACKGROUND The brain of major depressive disorder (MDD) is associated with altered functional connectivity (FC) compared to that of healthy individuals when processing positive and negative visual stimuli. Building upon alterations in brain connectivity, some researchers have employed electroencephalography (EEG) to study FC in MDD, aiming to enhance both diagnosis and treatment; however, the results have been inconsistent and the studies involving FC during emotional recognition are limited. This study aims to 1) investigate the effects of MDD on EEG patterns during visual emotional processing, 2) explore the therapeutic effects of antidepressant treatment on brain FC within the first week, and assess whether these effects can be predictive of treatment outcomes four weeks later, and 3) study baseline FC parameter biomarkers that can be used to predict treatment responsiveness in MDD patients. METHODS This clinical observational study recruited 38 healthy controls (HC) and 48 MDD patients. Patients underwent an EEG exam while looking at validated images of happy and sad faces at week 0 and 1. MDD patients were categorized into treatment responders and non-responders after 4 weeks of treatment. We conducted the FC analysis (node strength (NS), global efficiency (GE), and cluster coefficient (CC)) on HC and MDD patients using graph theoretical analysis. Multivariable linear regression was used to evaluate the influence of MDD on FC compared to HC, while controlling for confounding variables including age, gender, and academic degrees. RESULTS At week 0 and week 1, MDD patients revealed to have significant reductions in FC parameters (NS, GE and CC) compared to HC. When comparing MDD patients at week 1 post-antidepressant treatment and pre-treatment, no significant differences in FC changes were observed. Multivariable regression revealed a significant negative effect on FC of MDD. Compared to the treatment non-responsive group, the responsive group revealed a significantly higher FC in delta band frequency at baseline. CONCLUSIONS MDD patient group showed impaired FC during visual emotion-processing and we observed baseline FC parameters to be associated with treatment response at week 4. While signs of FC changes were observed in the brain after a week of treatment, it is possible that one week may still be insufficient to demonstrate significant alterations in the brain. Our results suggest the potential utilization of EEG-based FC as an indicative measure for predicting treatment response and monitoring treatment progress in MDD patients.
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Affiliation(s)
- Eunice J Yuan
- Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
| | | | - His-Han Chen
- Department of Psychiatry, Yang Ji Mental Hospital, Taiwan
| | - Shiau-Shian Huang
- Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan; Bali Psychiatric Center, Ministry of Health and Welfare, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; School of Public Health, National Defense Medical Center, Taipei, Taiwan.
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Teng CL, Cong L, Wang W, Cheng S, Wu M, Dang WT, Jia M, Ma J, Xu J, Hu WD. Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis. Front Hum Neurosci 2024; 18:1338765. [PMID: 38415279 PMCID: PMC10897049 DOI: 10.3389/fnhum.2024.1338765] [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: 12/23/2023] [Accepted: 01/25/2024] [Indexed: 02/29/2024] Open
Abstract
Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.
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Affiliation(s)
- Chao-Lin Teng
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, China
| | - Lin Cong
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, China
| | - Wei Wang
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Shan Cheng
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, 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, China
| | - Wei-Tao Dang
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, China
| | - Min Jia
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jin Ma
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, 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, China
| | - Wen-Dong Hu
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, 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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Huang SS, Yu YH, Chen HH, Hung CC, Wang YT, Chang CH, Peng SJ, Kuo PH. Functional connectivity analysis on electroencephalography signals reveals potential biomarkers for treatment response in major depression. BMC Psychiatry 2023; 23:554. [PMID: 37528355 PMCID: PMC10394892 DOI: 10.1186/s12888-023-04958-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/13/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND The treatment efficacy varies across individual patients with major depressive disorder (MDD). It lacks robust electroencephalography (EEG) markers for an antidepressant-responsive phenotype. METHOD This is an observational study enrolling 28 patients with MDD and 33 healthy controls (mean age of 40.7 years, and 71.4% were women). Patients underwent EEG exams at baseline (week0) and week1, while controls' EEG recordings were acquired only at week0. A resting eye-closing EEG segment was analyzed for functional connectivity (FC). Four parameters were used in FC analysis: (1) node strength (NS), (2) global efficiency (GE), (3) clustering coefficient (CC), and (4) betweenness centrality (BC). RESULTS We found that controls had higher values in delta wave in the indices of NS, GE, BC, and CC than MDD patients at baseline. After treatment with antidepressants, patients' FC indices improved significantly, including GE, mean CC, and mean NS in the delta wave. The FC in the alpha and beta bands of the responders were higher than those of the non-responders. CONCLUSIONS The FC of the MDD patients at baseline without treatment was worse than that of controls. After treatment, the FC improved and was close to the values of controls. Responders showed better FC in the high-frequency bands than non-responders, and this feature exists in both pre-treatment and post-treatment EEG.
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Affiliation(s)
- Shiau-Shian Huang
- Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Yu-Hsiang Yu
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - His-Han Chen
- Department of Psychiatry, Yang Ji Mental Hospital, Keelung, Taiwan
| | - Chia-Chun Hung
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Yao-Ting Wang
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Chieh Hsin Chang
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Syu-Jyun Peng
- Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan.
- Psychiatric Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [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: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Sakib N, Islam MK, Faruk T. Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:1701429. [PMID: 37293375 PMCID: PMC10247322 DOI: 10.1155/2023/1701429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 04/09/2023] [Accepted: 04/17/2023] [Indexed: 06/10/2023]
Abstract
Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8-30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.
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Affiliation(s)
- Nazmus Sakib
- Department of Electrical and Electronic Engineering, Independent University Bangladesh (IUB), Dhaka, Bangladesh
- Biomedical Instrumentation and Signal Processing Lab (BISPL), Independent University Bangladesh (IUB), Dhaka, Bangladesh
| | - Md Kafiul Islam
- Department of Electrical and Electronic Engineering, Independent University Bangladesh (IUB), Dhaka, Bangladesh
- Biomedical Instrumentation and Signal Processing Lab (BISPL), Independent University Bangladesh (IUB), Dhaka, Bangladesh
| | - Tasnuva Faruk
- Biomedical Instrumentation and Signal Processing Lab (BISPL), Independent University Bangladesh (IUB), Dhaka, Bangladesh
- Department of Public Health, Independent University Bangladesh (IUB), Dhaka, Bangladesh
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10
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Alterations in EEG functional connectivity in individuals with depression: A systematic review. J Affect Disord 2023; 328:287-302. [PMID: 36801418 DOI: 10.1016/j.jad.2023.01.126] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 01/22/2023] [Accepted: 01/30/2023] [Indexed: 02/19/2023]
Abstract
The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.
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Gao J, Chen M, Xiao D, Li Y, Zhu S, Li Y, Dai X, Lu F, Wang Z, Cai S, Wang J. Classification of major depressive disorder using an attention-guided unified deep convolutional neural network and individual structural covariance network. Cereb Cortex 2023; 33:2415-2425. [PMID: 35641181 DOI: 10.1093/cercor/bhac217] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 05/06/2022] [Accepted: 05/07/2022] [Indexed: 11/12/2022] Open
Abstract
Major depressive disorder (MDD) is the second leading cause of disability worldwide. Currently, the structural magnetic resonance imaging-based MDD diagnosis models mainly utilize local grayscale information or morphological characteristics in a single site with small samples. Emerging evidence has demonstrated that different brain structures in different circuits have distinct developmental timing, but mature coordinately within the same functional circuit. Thus, establishing an attention-guided unified classification framework with deep learning and individual structural covariance networks in a large multisite dataset could facilitate developing an accurate diagnosis strategy. Our results showed that attention-guided classification could improve the classification accuracy from primary 75.1% to ultimate 76.54%. Furthermore, the discriminative features of regional covariance connectivities and local structural characteristics were found to be mainly located in prefrontal cortex, insula, superior temporal cortex, and cingulate cortex, which have been widely reported to be closely associated with depression. Our study demonstrated that our attention-guided unified deep learning framework may be an effective tool for MDD diagnosis. The identified covariance connectivities and structural features may serve as biomarkers for MDD.
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Affiliation(s)
- Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Mingren Chen
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Die Xiao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yue Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shunli Zhu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yanling Li
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
| | - Xin Dai
- School of Automation, Chongqing University, Chongqing 400044, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhengning Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shimin Cai
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jiaojian Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
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12
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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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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14
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Zhu J, Wei S, Xie X, Yang C, Li Y, Li X, Hu B. Content-based multiple evidence fusion on EEG and eye movements for mild depression recognition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107100. [PMID: 36162244 DOI: 10.1016/j.cmpb.2022.107100] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/28/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Depression is a serious neurological disorder that has become a major health problem worldwide. The detection of mild depression is important for the diagnosis of depression in early stages. This research seeks to find a more accurate fusion model which can be used for mild depression detection using Electroencephalography and eye movement data. METHODS This study proposes a content-based multiple evidence fusion (CBMEF) method, which fuses EEG and eye movement data at decision level. The method mainly includes two modules, the classification performance matrix module and the dual-weight fusion module. The classification performance matrices of different modalities are estimated by Bayesian rule based on confusion matrix and Mahalanobis distance, and the matrices were used to correct the classification results. Then the relative conflict degree of each modality is calculated, and different weights are assigned to the above modalities at the decision fusion layer according to this conflict degree. RESULTS The experimental results show that the proposed method outperforms other fusion methods as well as the single modality results. The highest accuracies achieved 91.12%, and sensitivity, specificity and precision were 89.20%, 93.03%, 92.76%. CONCLUSIONS The promising results showed the potential of the proposed approach for the detection of mild depression. The idea of introducing the classification performance matrix and the dual-weight model to multimodal biosignals fusion casts a new light on the researches of depression recognition.
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Affiliation(s)
- Jing Zhu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Shiqing Wei
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiannian Xie
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Changlin Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yizhou Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; Shandong Academy Of Intelligent Computing Technoloy, Shandong, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, China; School of Medical Technology, Beijing Institute of Technology, Beijing, China.
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15
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EEG based depression recognition using improved graph convolutional neural network. Comput Biol Med 2022; 148:105815. [DOI: 10.1016/j.compbiomed.2022.105815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/11/2022] [Accepted: 07/03/2022] [Indexed: 11/19/2022]
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16
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Sun X, Zheng X, Li T, Li Y, Cui L. Multimodal Emotion Classification Method and Analysis of Brain Functional Connectivity Networks. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2022-2031. [PMID: 35857726 DOI: 10.1109/tnsre.2022.3192533] [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: 11/08/2022]
Abstract
Since multimodal emotion classification in different human states has rarely been studied, this paper explores the emotional mechanisms of the brain functional connectivity networks after emotional stimulation. We devise a multimodal emotion classification method fusing a brain functional connectivity network based on electroencephalography (EEG) and eye gaze (ECFCEG) to study emotional mechanisms. First, the nonlinear phase lag index (PLI) and phase-locked value (PLV) are calculated to construct the multiband brain functional connectivity networks, which are then converted into binary brain networks, and the seven features of the binary brain networks are extracted. At the same time, the features of the eye gaze signals are extracted. Then, a fusion algorithm called kernel canonical correlation analysis, based on feature level and randomization (FRKCCA), is executed for feature-level fusion (FLF) of brain functional connectivity networks and eye gaze. Finally, support vector machines (SVMs) are utilized to classify positive and negative emotions in multiple frequency bands with single modal features and multimodal features. The experimental results demonstrate that multimodal complementary representation properties can effectively improve the accuracy of emotion classification, achieving a classification accuracy of 91.32±1.81%. The classification accuracy of pupil diameter in the valence dimension is higher than that of additional features. In addition, the average emotion classification effect of the valence dimension is preferable to that of arousal. Our findings demonstrate that the brain functional connectivity networks of the right brain exhibit a deficiency. In particular, the information processing ability of the right temporal (RT) and right posterior (RP) regions is weak in the low frequency after emotional stimulation; Conversely, phase synchronization of the brain functional connectivity networks based on PLI is stronger than that of PLV.
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17
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Comparison of domain specific connectivity metrics for estimation brain network indices in boys with ADHD-C. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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18
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Wang Z, Ma Z, Liu W, An Z, Huang F. A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism. Brain Sci 2022; 12:834. [PMID: 35884641 PMCID: PMC9313113 DOI: 10.3390/brainsci12070834] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 02/01/2023] Open
Abstract
Depression is a common but easily misdiagnosed disease when using a self-assessment scale. Electroencephalograms (EEGs) provide an important reference and objective basis for the identification and diagnosis of depression. In order to improve the accuracy of the diagnosis of depression by using mainstream algorithms, a high-performance hybrid neural network depression detection method is proposed in this paper combined with deep learning technology. Firstly, a concatenating one-dimensional convolutional neural network (1D-CNN) and gated recurrent unit (GRU) are employed to extract the local features and to determine the global features of the EEG signal. Secondly, the attention mechanism is introduced to form the hybrid neural network. The attention mechanism assigns different weights to the multi-dimensional features extracted by the network, so as to screen out more representative features, which can reduce the computational complexity of the network and save the training time of the model while ensuring high precision. Moreover, dropout is applied to accelerate network training and address the over-fitting problem. Experiments reveal that the 1D-CNN-GRU-ATTN model has more effectiveness and a better generalization ability compared with traditional algorithms. The accuracy of the proposed method in this paper reaches 99.33% in a public dataset and 97.98% in a private dataset, respectively.
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Affiliation(s)
- Zhuozheng Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (Z.M.); (W.L.)
| | - Zhuo Ma
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (Z.M.); (W.L.)
| | - Wei Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (Z.M.); (W.L.)
| | - Zhefeng An
- Advising Center for Student Development, Beijing University of Technology, Beijing 100124, China;
| | - Fubiao Huang
- Department of Occupational Therapy, China Rehabilitation Research Center, Beijing 100068, China;
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19
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Liu W, Jia K, Wang Z, Ma Z. A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal. Brain Sci 2022; 12:630. [PMID: 35625016 PMCID: PMC9139403 DOI: 10.3390/brainsci12050630] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/29/2022] [Accepted: 05/09/2022] [Indexed: 11/16/2022] Open
Abstract
Depression has gradually become the most common mental disorder in the world. The accuracy of its diagnosis may be affected by many factors, while the primary diagnosis seems to be difficult to define. Finding a way to identify depression by satisfying both objective and effective conditions is an urgent issue. In this paper, a strategy for predicting depression based on spatiotemporal features is proposed, and is expected to be used in the auxiliary diagnosis of depression. Firstly, electroencephalogram (EEG) signals were denoised through the filter to obtain the power spectra of the three corresponding frequency ranges, Theta, Alpha and Beta. Using orthogonal projection, the spatial positions of the electrodes were mapped to the brainpower spectrum, thereby obtaining three brain maps with spatial information. Then, the three brain maps were superimposed on a new brain map with frequency domain and spatial characteristics. A Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) were applied to extract the sequential feature. The proposed strategy was validated with a public EEG dataset, achieving an accuracy of 89.63% and an accuracy of 88.56% with the private dataset. The network had less complexity with only six layers. The results show that our strategy is credible, less complex and useful in predicting depression using EEG signals.
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Affiliation(s)
- Wei Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (W.L.); (Z.W.); (Z.M.)
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
| | - Kebin Jia
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (W.L.); (Z.W.); (Z.M.)
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
| | - Zhuozheng Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (W.L.); (Z.W.); (Z.M.)
| | - Zhuo Ma
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (W.L.); (Z.W.); (Z.M.)
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20
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Gregorich M, Melograna F, Sunqvist M, Michiels S, Van Steen K, Heinze G. Individual-specific networks for prediction modelling – A scoping review of methods. BMC Med Res Methodol 2022; 22:62. [PMID: 35249534 PMCID: PMC8898441 DOI: 10.1186/s12874-022-01544-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 02/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space. Methods We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase. Results Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction. Conclusion The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01544-6.
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21
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The Effect of Music Listening on EEG Functional Connectivity of Brain: A Short-Duration and Long-Duration Study. MATHEMATICS 2022. [DOI: 10.3390/math10030349] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Music is considered a powerful brain stimulus, as listening to it can activate several brain networks. Music of different kinds and genres may have a different effect on the human brain. The goal of this study is to investigate the change in the brain’s functional connectivity (FC) when music is used as a stimulus. Secondly, the effect of listening to the subject’s favorite music is compared with listening to specifically formulated relaxing music with alpha binaural beats. Finally, the effect of the duration of music listening is studied. Subjects’ electroencephalographic (EEG) signals were captured as they listened to favorite and relaxing music. After preprocessing and artifact removal, the EEG recordings were decomposed into the delta, theta, alpha, and beta frequency bands, and the grand-averaged connectivity matrices were generated using Inter-Site Phase Clustering (ISPC) for each frequency band and each type of music. Furthermore, each lobe of the brain was analyzed separately to understand the effect of music on specific regions of the brain. EEG-FC among different channels was accessed by using graph theory and Network-based Statistics (NBS). To determine the significance of the changes in brain networks after listening to music, statistical analysis was conducted using Analysis of Variance (ANOVA) and t-test. The study of listening to music for a short duration verifies that either favorite or preferred music can affect the FC of the subject and induce a relaxation state. The short duration study also verifies a significant (ANOVA and t-test: p < 0.05) effectiveness of relaxing music over favorite music to induce relaxation and alertness in the subject. In the study of long duration, it is concluded that listening to relaxing music can increase functional connectivity and connections strength in the frontal lobe of the subject. A significant increase (ANOVA and t-test: p < 0.05) in FC in alpha and theta band and a significant decrease (ANOVA and t-test: p < 0.05) in FC in beta band in the frontal and parietal lobe of the brain verifies the hypothesis that the relaxing music can help the subject to achieve relaxation, activeness, and alertness.
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22
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Liu B, Chang H, Peng K, Wang X. An End-to-End Depression Recognition Method Based on EEGNet. Front Psychiatry 2022; 13:864393. [PMID: 35360138 PMCID: PMC8963113 DOI: 10.3389/fpsyt.2022.864393] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/14/2022] [Indexed: 11/18/2022] Open
Abstract
Major depressive disorder (MDD) is a common and highly debilitating condition that threatens the health of millions of people. However, current diagnosis of depression relies on questionnaires that are highly correlated with physician experience and hence not completely objective. Electroencephalography (EEG) signals combined with deep learning techniques may be an objective approach to effective diagnosis of MDD. This study proposes an end-to-end deep learning framework for MDD diagnosis based on EEG signals. We used EEG signals from 29 healthy subjects and 24 patients with severe depression to calculate Accuracy, Precision, Recall, F1-Score, and Kappa coefficient, which were 90.98%, 91.27%, 90.59%, and 81.68%, respectively. In addition, we found that these values were highest when happy-neutral face pairs were used as stimuli for detecting depression. Compared with exiting methods for EEG-based MDD classification, ours can maintain stable model performance without re-calibration. The present results suggest that the method is highly accurate for diagnosis of MDD and can be used to develop an automatic plug-and-play EEG-based system for diagnosing depression.
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Affiliation(s)
- Bo Liu
- Department of Emergency, The Second Hospital of Shandong University, Jinan, China
| | - Hongli Chang
- School of Information Science and Engineering, Southeast University, Nanjing, China
| | - Kang Peng
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xuenan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
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23
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Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2021.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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24
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Wang X, Liu W, Wang X, Mu Z, Xu J, Chang Y, Zhang Q, Wu J, Cong F. Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization. Front Hum Neurosci 2021; 15:799288. [PMID: 34975439 PMCID: PMC8714749 DOI: 10.3389/fnhum.2021.799288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/22/2021] [Indexed: 11/13/2022] Open
Abstract
Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.
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Affiliation(s)
- Xiulin Wang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Wenya Liu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
- Faculty of Information Technology, University of Jyväskylä, Jyvaskyla, Finland
| | - Xiaoyu Wang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Zhen Mu
- Department of Psychology, College of Humanities and Social Sciences, Dalian Medical University, Dalian, China
| | - Jing Xu
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Yi Chang
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Qing Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
- Faculty of Information Technology, University of Jyväskylä, Jyvaskyla, Finland
- School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
- Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, China
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25
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Wu CT, Huang HC, Huang S, Chen IM, Liao SC, Chen CK, Lin C, Lee SH, Chen MH, Tsai CF, Weng CH, Ko LW, Jung TP, Liu YH. Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset. BIOSENSORS 2021; 11:499. [PMID: 34940256 PMCID: PMC8699348 DOI: 10.3390/bios11120499] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/26/2021] [Accepted: 12/04/2021] [Indexed: 05/09/2023]
Abstract
Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.
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Affiliation(s)
- Chien-Te Wu
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo 113-0033, Japan;
| | - Hao-Chuan Huang
- Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan; (H.-C.H.); (S.H.); (C.-H.W.)
| | - Shiuan Huang
- Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan; (H.-C.H.); (S.H.); (C.-H.W.)
| | - I-Ming Chen
- Division of Psychosomatic Medicine, Department of Psychiatry, National Taiwan University Hospital, Taipei 100229, Taiwan; (I.-M.C.); (S.-C.L.)
- Institute of Health Policy and Management, National Taiwan University, Taipei 10617, Taiwan
| | - Shih-Cheng Liao
- Division of Psychosomatic Medicine, Department of Psychiatry, National Taiwan University Hospital, Taipei 100229, Taiwan; (I.-M.C.); (S.-C.L.)
| | - Chih-Ken Chen
- Department of Psychiatry & Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (C.-K.C.); (C.L.)
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
| | - Chemin Lin
- Department of Psychiatry & Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (C.-K.C.); (C.L.)
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
| | - Shwu-Hua Lee
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
- Department of Psychiatry, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (M.-H.C.); (C.-F.T.)
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan
| | - Chia-Fen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (M.-H.C.); (C.-F.T.)
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan
| | - Chang-Hsin Weng
- Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan; (H.-C.H.); (S.H.); (C.-H.W.)
| | - Li-Wei Ko
- Department of Bio Science & Tech., National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
| | - Tzyy-Ping Jung
- Institute for Neural Computation, University of California, San Diego, CA 92093, USA
| | - Yi-Hung Liu
- Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
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26
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Liu W, Wang X, Xu J, Chang Y, Hamalainen T, Cong F. Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1895-1904. [PMID: 34499604 DOI: 10.1109/tnsre.2021.3111564] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Neural oscillations exert a key role in coordinating the activity of remote brain regions, and various assemblies of oscillations can modulate different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of electroencephalography (EEG) oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillatory networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.
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27
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Chen X, Pan Z. A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6441. [PMID: 34198659 PMCID: PMC8296267 DOI: 10.3390/ijerph18126441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/10/2021] [Accepted: 06/10/2021] [Indexed: 12/12/2022]
Abstract
Depression is a common mental health disease, which has great harm to public health. At present, the diagnosis of depression mainly depends on the interviews between doctors and patients, which is subjective, slow and expensive. Voice data are a kind of data that are easy to obtain and have the advantage of low cost. It has been proved that it can be used in the diagnosis of depression. The voice data used for modeling in this study adopted the authoritative public data set, which had passed the ethical review. The features of voice data were extracted by Python programming, and the voice features were stored in the format of CSV files. Through data processing, a big database, containing 1479 voice feature samples, was generated for modeling. Then, the decision tree screening model of depression was established by 10-fold cross validation and algorithm selection. The experiment achieved 83.4% prediction accuracy on voice data set. According to the prediction results of the model, the patients can be given early warning and intervention in time, so as to realize the health management of personal depression.
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Affiliation(s)
- Xin Chen
- School of Medicine, Hangzhou Normal University, Hangzhou 311121, China;
- Engineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou Normal University, Hangzhou 311121, China
- Institute of VR and Intelligent System, Hangzhou Normal University, Hangzhou 311121, China
| | - Zhigeng Pan
- School of Medicine, Hangzhou Normal University, Hangzhou 311121, China;
- Engineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou Normal University, Hangzhou 311121, China
- Institute of VR and Intelligent System, Hangzhou Normal University, Hangzhou 311121, China
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28
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Lei L, Zhang Y, Song X, Liu P, Wen Y, Zhang A, Yang C, Sun N, Liu Z, Zhang K. Face Recognition Brain Functional Connectivity in Patients With Major Depression: A Brain Source Localization Study by ERP. Front Psychiatry 2021; 12:662502. [PMID: 34803748 PMCID: PMC8604097 DOI: 10.3389/fpsyt.2021.662502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 09/30/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Patients with major depressive disorder (MDD) presents with face recognition defects. These defects negatively affect their social interactions. However, the cause of these defects is not clear. This study sought to explore whether MDD patients develop facial perceptual processing disorders with characteristics of brain functional connectivity (FC). Methods: Event-related potential (ERP) was used to explore differences between 20 MDD patients and 20 healthy participants with face and non-face recognition tasks based on 64 EEG parameters. After pre-processing of EEG data and source reconstruction using the minimum-norm estimate (MNE), data were converted to AAL90 template to obtain a time series of 90 brain regions. EEG power spectra were determined using Fieldtrip incorporating a Fast Fourier transform. FC was determined for all pairs of brain signals for theta band using debiased estimate of weighted phase-lag index (wPLI) in Fieldtrip. To explore group differences in wPLI, independent t-tests were performed with p < 0.05 to indicate statistical significance. False discovery rate (FDR) correction was used to adjust p-values. Results: The findings showed that amplitude induction by face pictures was higher compared with that of non-face pictures both in MDD and healthy control (HC) groups. Face recognition amplitude in MDD group was lower compared with that in the HC group. Two time periods with significant differences were then selected for further analysis. Analysis showed that FC was stronger in the MDD group compared with that in the HC group in most brain regions in both periods. However, only one FC between two brain regions in HC group was stronger compared with that in the MDD group. Conclusion: Dysfunction in brain FC among MDD patients is a relatively complex phenomenon, exhibiting stronger and multiple connectivity with several brain regions of emotions. The findings of the current study indicate that the brain FC of MDD patients is more complex and less efficient in the initial stage of face recognition.
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Affiliation(s)
- Lei Lei
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Yu Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Xiaotong Song
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Penghong Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Yujiao Wen
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Aixia Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Chunxia Yang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Ning Sun
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Zhifen Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
| | - Kerang Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.,Shanxi Medical University, Taiyuan, China
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29
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Zhang B, Yan G, Yang Z, Su Y, Wang J, Lei T. Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification. IEEE Trans Neural Syst Rehabil Eng 2020; 29:215-229. [PMID: 33296307 DOI: 10.1109/tnsre.2020.3043426] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.
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