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Association between Electroencephalographic microstates abnormalities and cognitive dysfunction in drug-naive MDD

Brain Research 2025年 1860卷 149660-149660页

作者： Qin, Qin Liu, Xinyu Wang, Bin Wang, Xin Liang, Sixiang Chen, Chao Li, Meijia Han, Chuanliang Zhao, Xixi National Clinical Research Center for Mental Disorders & National Center for Mental Disorders Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Advanced Innovation Center for Human Brain Protection Capital Medical University Beijing China Department of Clinical Psychology Shandong Provincial Hospital affiliated to Shandong First Medical University Shandong China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin 300384 China Faculty of Psychology and Center for Neuroscience Vrije Universiteit Brussel Brussels 1050 Belgium School of Biomedical Sciences and The Gerald Choa Neuroscience Institute The Chinese University of Hong Kong Hong Kong SAR China

Objectives: This study aims to investigate the link between Electroencephalography (EEG) microstate anomalies and cognitive impairments in individuals with drug-naive Major depressive disorder (MDD). Methods: We recruited 29 patients with drug-naive MDD and 30 healthy controls. The Hamilton Depression Rating Scale (HDRS-17) measured symptom severity, the MATRICS Consensus Cognitive Battery (MCCB) assessed neurocognitive function, and resting-state EEG data were collected using 64 scalp electrodes. Analysis of EEG microstates was conducted via the Microstate Analysis plugin for EEGLAB. Results: MDD group had lower scores in six neurocognitive MCCB domains. For EEG microstates, four similar ones (A − D) were found in both groups. Notably, microstate C duration was lower in MDD group (t = 4.549, P < 0.001), microstate D occurrence (t = 2.258, P = 0.028) and proportion (t = 3.733, P < 0.001) were lower in MDD group. There were significant differences in all 4 microstate transition probabilities between groups. For example, A − B, B − A etc. transitions were higher in MDD, while A − C, A − D etc. were lower. The proportion of microstate D was found positively correlated with Speed of processing (SOP) score (r = 0.499, df = 26, P = 0.007) and Working memory (WM) score (r = 0.451, df = 26, P = 0.016). The Occurrence of microstate D was found positively correlated with SOP score (r = 0.383, df = 26, P = 0.044) and WM score (r = 0.389, df = 26, P = 0.041). Conclusions: MDD patients show alterations in sub-second brain dynamics, characterized by a decreased proportion and occurrence of microstate D and shorter duration of microstate C, and significant shifts in microstate transition probabilities. These changes correlate with cognitive deficits across several domains, including processing speed and working memory. © 2025

关键词： Cognitive dysfunction Electroencephalographic microstates Major depressive disorder

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A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

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Biomedical Signal Processing and control 2022年第PartB期72卷 103370-103370页

作者： Lei, Yu Belkacem, Abdelkader Nasreddine Wang, Xiaotian Sha, Sha Wang, Changming Chen, Chao Beijing Institute of Basic Medical Sciences Beijing China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Xidian University Xian China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Brain-inspired Intelligence and Clinical Translational Research Center/Department of Neurosurgery Xuanwu Hospital Capitap Medical University Beijing China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders. © 2021 Elsevier Ltd

关键词： Deep neural networks

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Classification of Cognitive Ability of Healthy Elderly Individuals Using Resting-State Functional Connectivity Magnetic Resonance Imaging and An Extreme Learning Machine

Research Square

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Research Square 2021年

作者： Yang, Zekun Ge, Manling Abdelkader, Nasreddine Belkacem Fu, Xiaoxuan Zhang, Qirui Chen, Shenghua Xie, Chong Song, Zibo Chen, Chao State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology Tianjin300130 China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School CharlestownMA02129 United States Department of Medical Imaging Jinling Hospital Nanjing University School of Medicine General Hospital of Eastern Theater Nanjing210002 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300003 China

Purpose Quantitative determination of the correlation between cognitive ability and functional biomarkers in the elderly brain is essential. To identify biomarkers associated with cognitive performance in the elderly, this study combined an index model specific for resting-state functional connectivity (FC) magnetic resonance imaging (fcMRI) with a supervised machine learning method. Methods Performance scores on conventional cognitive test batteries and MRI data were obtained for 98 healthy elderly individuals and 90 healthy youth from two public databases. Based on the test scores, the elderly cohort was categorized into two groups: excellent and poor. An fcMRI index model was constructed for each elderly individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using the fcMRI indexes. To confirm the effectiveness of constructed model, the fcMRI indexes of these brain areas were used as feature matrix inputs for training an extreme learning machine. Classification accuracy was then tested in separate groups and confirmed by N-fold cross-validation. Results This learning study could effectively classify the cognitive status of healthy elderly individuals according to frontal lobe, temporal lobe, and parietal lobe FC values with a mean accuracy of 83.5%, which is substantially higher than that achieved using conventional correlation analysis. Conclusion This fcMRI classification study may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms. © 2021, CC BY.

关键词： Magnetic resonance imaging

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End-to-End AI-Based Point-of-Care Diagnosis system for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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A Method for Obtaining Contact Force between Catheter Tip and Vascular Wall in Master-slave Robotic system

A Method for Obtaining Contact Force between Catheter Tip an...

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IEEE International Conference on Mechatronics and Automation

作者： Xiaoliang Jin Shuxiang Guo Jian Guo Peng Shi Dapeng Song Faculty of Engineering and Design Kagawa University 2217-20 Hayashi-cho Takamatsu Kagawa Japan Key Laboratory of Convergence Medical Engineering System and Healthcare Technology the Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology Beijing China Tianjin Key Laboratory for Control Theory and Application in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In the research of vascular interventional surgery robotic system, the force information acquisition of surgical tools (guidewires and catheters) has a significant affect on the safety of surgery, especially the contact force between the catheter tip and vascular wall. Therefore. In order to obtain this contact force, we noticed the pressure sensitive rubber because of its piezoresistive effect and easy processing into any shape. a force sensor based on the pressure sensitive rubber was developed in this paper. Once a force is applied on the force sensor attached to the catheter tip, the contact force can be approximated by the piezoresistive effect of the pressure sensitive rubber. The feasibility of the developed force sensor was verified by the experiments in “Vitro”. And the results showed that the force sensor was developed in this paper can accurately detect the contact force between the catheter tip and vascular wall at different positions in the vascular model, the extraction of contact force information of catheter tip is of great significance to the safety of surgery, and the application potential of pressure sensitive rubber.

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Path Planning for Mobile Robots Based on Improved RRT Algorithm

Path Planning for Mobile Robots Based on Improved RRT Algori...

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IEEE International Conference on Mechatronics and Automation

作者： Lei Shao Hanze Liu Chao Chen Dongdong Du Ji Li Hongli Liu Tianjin Key Laboratory for Control Theory & Applications in Complicated system Tianjin University of Technology Binshui Xidao 391 Tianjin China China Academy of Industrial Internet Institute of Data management and application Beijing Tower B East Bay International Center No.6 Liza West Street Chaoyang District Beijing

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Aiming at the shortcomings of the basic RRT algorithm, such as the random search ability is relatively strong, and the calculation result is usually not the shortest path, etc., an improved RRT path optimization algorithm is proposed, which uses the ant colony algorithm to obtain the basic RRT algorithm. The path is iterated and optimized, and eventually converges to a shortest path, improving the efficiency of planning the shortest path in advance, and conducting in-depth research on this algorithm. A large number of simulation results show that the improved algorithm has certain feasibility and effectiveness. In multiple iterations, it can eventually converge to a path with a better distance.

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Research on intelligent monitoring system ofgreenhouse intensity and CO2 concentration based on STM32 15

Research on intelligent monitoring system ofgreenhouse inten...

引用

15th IEEE International Conference on Mechatronics and Automation, ICMA 2018

作者： Li, Shuanzeng School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin Key Laboratory of complex system control theory and Application Binshui Xidao 391 Tianjin300384 China

ISBN: (纸本)9781538660720

Because the stability and accuracy of the traditional greenhouse monitoring system are not high enough, the light intensity and the grape canopy of the intelligent monitoring system are established for this phenomenon. It is based on the C02 concentration of STM32 single chip to monitor the intensity of light and C02 concentration and show it at the same time. Serial communication is used to send test results to MCGS touch screen to achieve real-time monitoring and warning of light intensity and C02 concentration in greenhouse. The result of long term test shows the precision of the light intensity detection of the system is 940lux, C02 concentration measurement accuracy 40ppm. Compared with the traditional automatic monitoring system, the system runs stably and costs less, and the accuracy meets the requirements of grape greenhouse. In conclusion, the system proposed in this paper is suitable for agriculture. © 2018 IEEE.

关键词： Greenhouses

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Development of the Insect-inspired Biomimetic Underwater Microrobot for a Father-son Robot system

Development of the Insect-inspired Biomimetic Underwater Mic...

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IEEE International Conference on Mechatronics and Automation

作者： Wenbo Sui Shuxiang Guo Liang Zheng Ruochen An Awa Tendeng Graduate School of Engineering Kagawa University Takamatsu Kagawa Japan Faculty of Engineering and Design Kagawa University Hayashi-cho Kagawa Japan Key Laboratory of Convergence Medical Engineering System and Healthcare Technology the Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology Beijing China Tianjin Key Laboratory for Control Theory and Application in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In this paper, the lifting platform structure of the father robot and the development of the insect-inspired biomimetic underwater microrobot are presented. The objective is to decrease the load of microrobot and realize more functions. In previous researchers, the platform of the father robot is fixed, which is not conducive to adopt and release the son robot. The structure of the lifting platform can effectively solve the problem in theory. The insect-inspired microrobot used wood which has high hydrophilism. It causes the body of microrobot increased weight and short-term works. The fillable structure with waterproof character which is made by a 3D printer. The new structure also can be changed weight for achieving different functions underwater efficiently. Then, two kinds of communication between the light source and microrobot like chasing rotation and fixed distance chasing are described in order to prove the feasibility of the software. In all these cases, experimental results show the performance of the microrobot.

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Design of LT-Med seawater desalination temperature control system based on dynamic matrix predictive fuzzy PID control algorithm 15

Design of LT-Med seawater desalination temperature control s...

引用

15th IEEE International Conference on Mechatronics and Automation, ICMA 2018

作者： Liu, Hongli Song, Lulu Shao, Lei Tan, Zhenhai Chen, Xiaoqi Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Applications Tianjin University of Technology Binshui Xidao 391 Tianjin China

ISBN: (纸本)9781538660720

This article takes the "Twelfth Five-Year"50t/d MED pilot plant as the research background. According to the characteristics of nonlinearity, large inertia, large delay, and difficulty in establishing accurate mathematical models of temperature control systems in low-temperature multiple-effect (LT-MED) desalination plants, we have analyzed dynamic matrix predictive control and self-tuning fuzzy PID control methods. It is proposed to combine the two methods to achieve the effect of temperature *** the simulation test, the results show that the dynamic matrix fuzzy PID controller is more effective than the general fuzzy PID controller. For example, the adjustment time is effectively reduced, overshoot is almost zero enhances and the anti-interference ability is also *** fuzzy controller added to the dynamic matrix can better achieve the control effect we need in industrial production. © 2018 IEEE.

关键词： Temperature control

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Energy Consumption Data Prediction Analysis based on EEMD-ARMA Model

Energy Consumption Data Prediction Analysis based on EEMD-AR...

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IEEE International Conference on Mechatronics and Automation

作者： Ji Li Xudong Jiang Lei Shao Hongli Liu Chao Chen Gang Wang Dongdong Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin University of Technology Binshui Xidao 391 Tianjin China Tianjin Economic and Technological Development Zone Binhai New Area Continental Automotive (System) Tianjin Co. Ltd. Continental Automotive (System) Tianjin Co. Ltd No.2 Bohai Road Tianjin China China Academy of Industrial Internet Institute of Data management and application Beijing Block B East Lake International Center No. 6 Lize West Street Chaoyang District Beijing

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Energy consumption data prediction model based on EEMD-ARMA prediction model is used to predict the nonlinear and non-stationary characteristics of industrial energy consumption data, which leads to the difficulty of data prediction and low prediction accuracy. This paper improves empirical mode decomposition (empirical mode decomposition, EMD) by ensemble empirical mode decomposition (ensemble empirical mode decomposition, EEMD) and predicts energy consumption by combining auto regressive sliding average model (auto regressive and moving average model, ARMA). Finally, the historical data of a certain system is selected as a sample, and the comparative analysis of EMD-ARMA and EEMD-ARMA prediction results is used to verify that the EEMD-ARMA algorithm used is more accurate than the prediction algorithm, and its prediction results have certain reference value for the control of energy consumption data in enterprises.

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