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Gu C, Chou T, Widge AS, Dougherty DD. EEG complexity in emotion conflict task in individuals with psychiatric disorders. Behav Brain Res 2024; 467:114997. [PMID: 38621461 DOI: 10.1016/j.bbr.2024.114997] [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: 08/15/2023] [Revised: 03/01/2024] [Accepted: 04/09/2024] [Indexed: 04/17/2024]
Abstract
Analyzing EEG complexity may help to elucidate complex brain dynamics in individuals with psychiatric disorders and provide insight into neural connectivity and its relationship with deficits such as emotion-related impulsivity. EEG complexity was calculated through multiscale entropy and compared between a heterogeneous psychiatric patient group and a healthy control group during the emotion conflict resolution task. Twenty-eight healthy adults and ten psychiatric patients were recruited and compared on the multiscale entropy of EEG acquired in the task. Our results revealed a lower multiscale entropy in the psychiatric patient group compared to the healthy group during the task. This decrease in multiscale entropy suggests reduced long-range interaction between the left frontal region and other brain regions during the emotion conflict resolution task among psychiatric patients. Notably, a positive correlation was observed between multiscale entropy and impulsivity measures in the psychiatric patient group, where the higher the EEG complexity during the emotion regulation task, the higher the level of self-reported impulsivity in the psychiatric patients. Such impulsivity was evident in both healthy individuals and psychiatric patients, with healthy individuals showing shorter reaction times on incongruent conditions compared to congruent conditions and psychiatric patients displaying similar reaction times in both conditions, This study highlights the significance of investigating EEG complexity and its potential applications in the transdiagnostic exploration of impulsivity in psychiatric disorders.
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Affiliation(s)
- Chao Gu
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, USA.
| | - Tina Chou
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, USA
| | - Alik S Widge
- Department of Psychiatry, University of Minnesota, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, USA
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2
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Luo X, Zhou B, Fang J, Cherif-Riahi Y, Li G, Shen X. Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach. Diagnostics (Basel) 2024; 14:1122. [PMID: 38893648 PMCID: PMC11172130 DOI: 10.3390/diagnostics14111122] [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: 04/06/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment strategies. To address this need, this study aims to establish a GAD grading and quantification diagnostic model by integrating an electroencephalogram (EEG) and ensemble learning. In this context, a total of 39 normal subjects and 80 GAD patients were recruited and divided into four groups: normal control, mild GAD, moderate GAD, and severe GAD. Ten minutes resting state EEG data were collected for every subject. Functional connectivity features were extracted from each EEG segment with different time windows. Then, ensemble learning was employed for GAD classification studies and brain mechanism analysis. Hence, the results showed that the Catboost model with a 10 s time window achieved an impressive 98.1% accuracy for four-level classification. Particularly, it was found that those functional connections situated between the frontal and temporal lobes were significantly more abundant than in other regions, with the beta rhythm being the most prominent. The analysis framework and findings of this study provide substantial evidence for the applications of artificial intelligence in the clinical diagnosis of GAD.
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Affiliation(s)
- Xiaodong Luo
- The Second Hospital of Jinhua, Jinhua 321016, China;
| | - Bin Zhou
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China;
| | - Jiaqi Fang
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China;
| | - Yassine Cherif-Riahi
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China;
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China;
| | - Xueqian Shen
- The Second Hospital of Jinhua, Jinhua 321016, China;
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3
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Al-Zamil M, Kulikova NG, Minenko IA, Shurygina IP, Petrova MM, Mansur N, Kuliev RR, Blinova VV, Khripunova OV, Shnayder NA. Comparative Analysis of High-Frequency and Low-Frequency Transcutaneous Electrical Stimulation of the Right Median Nerve in the Regression of Clinical and Neurophysiological Manifestations of Generalized Anxiety Disorder. J Clin Med 2024; 13:3026. [PMID: 38892737 PMCID: PMC11172620 DOI: 10.3390/jcm13113026] [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: 04/10/2024] [Revised: 05/15/2024] [Accepted: 05/19/2024] [Indexed: 06/21/2024] Open
Abstract
Background/Objectives: The anxiolytic effect of transcutaneous electrical nerve stimulation (TENS) is associated with the activation of endogenous inhibitory mechanisms in the central nervous system. Both low-frequency, high-amplitude TENS (LF-TENS) and high-frequency, low-amplitude TENS (HF-TENS) are capable of activating opioid, GABA, serotonin, muscarinic, and cannabinoid receptors. However, there has been no comparative analysis of the effectiveness of HF-TENS and LF-TENS in the treatment of GAD. The purpose of our research was to study the effectiveness of direct HF-TENS and LF-TENS of the right median nerve in the treatment of patients with GAD compared with sham TENS. Methods: The effectiveness of direct HF-TENS and LF-TENS of the right median nerve in the treatment of GAD was studied using Generalized Anxiety Disorder 7-item scale (GAD-7) and the Hamilton Anxiety Rating Scale (HAM-A). 40 patients underwent sham TENS, 40 patients passed HF-TENS (50 Hz-50 μs-sensory response) and 41 patients completed LF -TENS (1 Hz-200 μs-motor response) for 30 days daily. After completion of treatment, half of the patients received weekly maintenance therapy for 6 months. Electroencephalography was performed before and after treatment. Results: Our study showed that a significant reduction in the clinical symptoms of GAD as assessed by GAD-7 and HAM-A was observed after HF-TENS and LF-TENS by an average of 42.4%, and after sham stimulation only by 13.5% for at least 2 months after the end of treatment. However, LF-TENS turned out to be superior in effectiveness to HF-TENS by 51% and only on electroencephalography leads to an increase in PSD for the alpha rhythm in the occipital regions by 24% and a decrease in PSD for the beta I rhythm in the temporal and frontal regions by 28%. The prolonged effect of HF-TENS and LF-TENS was maintained without negative dynamics when TENS treatment was continued weekly throughout the entire six-month observation period. Conclusions: A prolonged anxiolytic effect of direct TENS of the right median nerve has been proven with greater regression of clinical and neurophysiological manifestations of GAD after LF-TENS compared to HF-TENS. Minimal side effects, low cost, safety, and simplicity of TENS procedures are appropriate as a home treatment modality.
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Affiliation(s)
- Mustafa Al-Zamil
- Department of Physiotherapy, Faculty of Continuing Medical Education, Peoples’ Friendship University of Russia, 117198 Moscow, Russia; (N.G.K.); (N.M.); (V.V.B.)
| | - Natalia G. Kulikova
- Department of Physiotherapy, Faculty of Continuing Medical Education, Peoples’ Friendship University of Russia, 117198 Moscow, Russia; (N.G.K.); (N.M.); (V.V.B.)
- Department of Sports Medicine and Medical Rehabilitation, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia; (I.A.M.); (O.V.K.)
| | - Inessa A. Minenko
- Department of Sports Medicine and Medical Rehabilitation, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia; (I.A.M.); (O.V.K.)
- Department of Restorative Medicine and Neurorehabilitation, Medical Dental Institute, 127253 Moscow, Russia;
| | - Irina P. Shurygina
- Department of Ophthalmology, Rostov State Medical University, 344022 Rostov, Russia;
| | - Marina M. Petrova
- Shared Core Facilities “Molecular and Cell Technologies”, Professor V. F. Voino-Yasenetsky Krasnoyarsk State Medical University, 660022 Krasnoyarsk, Russia;
| | - Numman Mansur
- Department of Physiotherapy, Faculty of Continuing Medical Education, Peoples’ Friendship University of Russia, 117198 Moscow, Russia; (N.G.K.); (N.M.); (V.V.B.)
- Department of Restorative Medicine and Neurorehabilitation, Medical Dental Institute, 127253 Moscow, Russia;
- City Clinical Hospital Named after V. V. Vinogradov, 117292 Moscow, Russia
| | - Rufat R. Kuliev
- Department of Restorative Medicine and Neurorehabilitation, Medical Dental Institute, 127253 Moscow, Russia;
| | - Vasilissa V. Blinova
- Department of Physiotherapy, Faculty of Continuing Medical Education, Peoples’ Friendship University of Russia, 117198 Moscow, Russia; (N.G.K.); (N.M.); (V.V.B.)
- Department of Restorative Medicine and Neurorehabilitation, Medical Dental Institute, 127253 Moscow, Russia;
| | - Olga V. Khripunova
- Department of Sports Medicine and Medical Rehabilitation, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia; (I.A.M.); (O.V.K.)
| | - Natalia A. Shnayder
- Shared Core Facilities “Molecular and Cell Technologies”, Professor V. F. Voino-Yasenetsky Krasnoyarsk State Medical University, 660022 Krasnoyarsk, Russia;
- Institute of Personalized Psychiatry and Neurology, V.M. Bekhterev National Medical Research Centre for Psychiatry and Neurology, 192019 Saint Petersburg, Russia
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Baygin M, Barua PD, Dogan S, Tuncer T, Hong TJ, March S, Tan RS, Molinari F, Acharya UR. Automated anxiety detection using probabilistic binary pattern with ECG signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108076. [PMID: 38422891 DOI: 10.1016/j.cmpb.2024.108076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/22/2024] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND AND AIM Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals. MATERIALS AND METHODS We studied open-access electrocardiography (ECG) data of 19 subjects collected via wearable sensors while they were shown videos that might induce anxiety. Using the Hamilton Anxiety Rating Scale, subjects are categorized into normal, light anxiety, moderate anxiety, and severe anxiety groups. ECGs were divided into non-overlapping 4- (Case 1), 5- (Case 2), and 6-second (Case 3) segments for analysis. We proposed a self-organized dynamic pattern-based feature extraction function-probabilistic binary pattern (PBP)-in which patterns within the function were determined by the probabilities of the input signal-dependent values. This was combined with tunable q-factor wavelet transform to facilitate multileveled generation of feature vectors in both spatial and frequency domains. Neighborhood component analysis and Chi2 functions were used to select features and reduce data dimensionality. Shallow k-nearest neighbors and support vector machine classifiers were used to calculate four (=2 × 2) classifier-wise results per input signal. From the latter, novel self-organized combinational majority voting was applied to calculate an additional five voted results. The optimal final model outcome was chosen from among the nine (classifier-wise and voted) results using a greedy algorithm. RESULTS Our model achieved classification accuracies of over 98.5 % for all three cases. Ablation studies confirmed the incremental accuracy of PBP-based feature engineering over traditional local binary pattern feature extraction. CONCLUSIONS The results demonstrated the feasibility and accuracy of our PBP-based feature engineering model for anxiety classification using ECG signals.
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Affiliation(s)
- Mehmet Baygin
- Department of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, Erzurum, Turkey
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, 23119, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, 23119, Elazig, Turkey
| | - Tan Jen Hong
- Data Science and Artificial Intelligence Lab, Singapore General Hospital, Singapore
| | - Sonja March
- Centre for Health Research, University of Southern Queensland, Australia; School of Psychology and Wellbeing, University of Southern Queensland, Australia
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - Filippo Molinari
- Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - U Rajendra Acharya
- Centre for Health Research, University of Southern Queensland, Australia; School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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5
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Yoo JH, Jeong H, An JH, Chung TM. Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models. SENSORS (BASEL, SWITZERLAND) 2024; 24:715. [PMID: 38276406 PMCID: PMC10818263 DOI: 10.3390/s24020715] [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: 12/20/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 01/27/2024]
Abstract
The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status.
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Affiliation(s)
- Joo Hun Yoo
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Republic of Korea;
- Hippo T&C Inc., Suwon 16419, Republic of Korea;
| | - Harim Jeong
- Hippo T&C Inc., Suwon 16419, Republic of Korea;
- Department of Interaction Science, Sungkyunkwan University, Seoul 03063, Republic of Korea
| | - Ji Hyun An
- Department of Psychiatry, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Tai-Myoung Chung
- Hippo T&C Inc., Suwon 16419, Republic of Korea;
- Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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Liu W, Li G, Huang Z, Jiang W, Luo X, Xu X. Enhancing generalized anxiety disorder diagnosis precision: MSTCNN model utilizing high-frequency EEG signals. Front Psychiatry 2023; 14:1310323. [PMID: 38179243 PMCID: PMC10764566 DOI: 10.3389/fpsyt.2023.1310323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/01/2023] [Indexed: 01/06/2024] Open
Abstract
Generalized Anxiety Disorder (GAD) is a prevalent mental disorder on the rise in modern society. It is crucial to achieve precise diagnosis of GAD for improving the treatments and averting exacerbation. Although a growing number of researchers beginning to explore the deep learning algorithms for detecting mental disorders, there is a dearth of reports concerning precise GAD diagnosis. This study proposes a multi-scale spatial-temporal local sequential and global parallel convolutional model, named MSTCNN, which designed to achieve highly accurate GAD diagnosis using high-frequency electroencephalogram (EEG) signals. To this end, 10-min resting EEG data were collected from 45 GAD patients and 36 healthy controls (HC). Various frequency bands were extracted from the EEG data as the inputs of the MSTCNN. The results demonstrate that the proposed MSTCNN, combined with the attention mechanism of Squeeze-and-Excitation Networks, achieves outstanding classification performance for GAD detection, with an accuracy of 99.48% within the 4-30 Hz EEG data, which is competitively related to state-of-art methods in terms of GAD classification. Furthermore, our research unveils an intriguing revelation regarding the pivotal role of high-frequency band in GAD diagnosis. As the frequency band increases, diagnostic accuracy improves. Notably, high-frequency EEG data ranging from 10-30 Hz exhibited an accuracy rate of 99.47%, paralleling the performance of the broader 4-30 Hz band. In summary, these findings move a step forward towards the practical application of automatic diagnosis of GAD and provide basic theory and technical support for the development of future clinical diagnosis system.
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Affiliation(s)
- Wei Liu
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Ziyi Huang
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Weixiong Jiang
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | | | - Xingjuan Xu
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
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7
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Li X, Kang Q, Gu H. A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder. Front Hum Neurosci 2023; 17:1280512. [PMID: 38021236 PMCID: PMC10646310 DOI: 10.3389/fnhum.2023.1280512] [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: 08/20/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.
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Affiliation(s)
- Xuanyi Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Qiang Kang
- Department of Radiology, Xing’an League People’s Hospital of Inner Mongolia, Mongolia, China
| | - Hanxing Gu
- Department of Geriatric Psychiatry, Qingdao Mental Health Center, Qingdao, Shandong, China
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Xu Y, Zhong H, Ying S, Liu W, Chen G, Luo X, Li G. Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:8639. [PMID: 37896732 PMCID: PMC10611358 DOI: 10.3390/s23208639] [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: 09/07/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023]
Abstract
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.
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Affiliation(s)
- Yanting Xu
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (Y.X.); (S.Y.)
| | - Hongyang Zhong
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Shangyan Ying
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (Y.X.); (S.Y.)
| | - Wei Liu
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Guibin Chen
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Xiaodong Luo
- The Second Hospital of Jinhua, Jinhua 321016, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
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Obukhov A, Krasnyanskiy M, Volkov A, Nazarova A, Teselkin D, Patutin K, Zajceva D. Method for Assessing the Influence of Phobic Stimuli in Virtual Simulators. J Imaging 2023; 9:195. [PMID: 37888302 PMCID: PMC10607658 DOI: 10.3390/jimaging9100195] [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: 07/31/2023] [Revised: 09/17/2023] [Accepted: 09/22/2023] [Indexed: 10/28/2023] Open
Abstract
In the organizing of professional training, the assessment of the trainee's reaction and state in stressful situations is of great importance. Phobic reactions are a specific type of stress reaction that, however, is rarely taken into account when developing virtual simulators, and are a risk factor in the workplace. A method for evaluating the impact of various phobic stimuli on the quality of training is considered, which takes into account the time, accuracy, and speed of performing professional tasks, as well as the characteristics of electroencephalograms (the amplitude, power, coherence, Hurst exponent, and degree of interhemispheric asymmetry). To evaluate the impact of phobias during experimental research, participants in the experimental group performed exercises in different environments: under normal conditions and under the influence of acrophobic and arachnophobic stimuli. The participants were divided into subgroups using clustering algorithms and an expert neurologist. After that, a comparison of the subgroup metrics was carried out. The research conducted makes it possible to partially confirm our hypotheses about the negative impact of phobic effects on some participants in the experimental group. The relationship between the reaction to a phobia and the characteristics of brain activity was revealed, and the characteristics of the electroencephalogram signal were considered as the metrics for detecting a phobic reaction.
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Affiliation(s)
- Artem Obukhov
- The Laboratory of Medical VR Simulator Systems for Training, Diagnostics and Rehabilitation, Tambov State Technical University, Tambov 392000, Russia; (M.K.); (A.V.); (A.N.); (D.T.); (K.P.); (D.Z.)
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10
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Qi X, Xu W, Li G. Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder. Brain Sci 2023; 13:1282. [PMID: 37759883 PMCID: PMC10526432 DOI: 10.3390/brainsci13091282] [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: 07/23/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.
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Affiliation(s)
- Xuchen Qi
- Department of Neurosurgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China;
- Department of Neurosurgery, Shaoxing People’s Hospital, Shaoxing 312000, China
| | - Wanxiu Xu
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China;
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
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11
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Qi X, Fang J, Sun Y, Xu W, Li G. Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder. Diagnostics (Basel) 2023; 13:diagnostics13071292. [PMID: 37046509 PMCID: PMC10093329 DOI: 10.3390/diagnostics13071292] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 04/01/2023] Open
Abstract
To investigate the differences in functional brain network structures between patients with a high level of generalized anxiety disorder (HGAD) and those with a low level of generalized anxiety disorder (LGAD), a resting-state electroencephalogram (EEG) was recorded in 30 LGAD patients and 21 HGAD patients. Functional connectivity between all pairs of brain regions was determined by the Phase Lag Index (PLI) to construct a functional brain network. Then, the characteristic path length, clustering coefficient, and small world were calculated to estimate functional brain network structures. The results showed that the PLI values of HGAD were significantly increased in alpha2, and significantly decreased in the theta and alpha1 rhythms, and the small-world attributes for both HGAD patients and LGAD patients were less than one for all the rhythms. Moreover, the small-world values of HGAD were significantly lower than those of LGAD in the theta and alpha2 rhythms, which indicated that the brain functional network structure would deteriorate with the increase in generalized anxiety disorder (GAD) severity. Our findings may play a role in the development and understanding of LGAD and HGAD to determine whether interventions that target these brain changes may be effective in treating GAD.
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Affiliation(s)
- Xuchen Qi
- Department of Neurosurgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
- Department of Neurosurgery, Shaoxing People’s Hospital, Shaoxing 312000, China
| | - Jiaqi Fang
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310000, China
| | - Wanxiu Xu
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
- Correspondence: (W.X.); (G.L.)
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
- Correspondence: (W.X.); (G.L.)
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Wang J, Fang J, Xu Y, Zhong H, Li J, Li H, Li G. Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder. Front Hum Neurosci 2022; 16:1074587. [PMID: 36504623 PMCID: PMC9731337 DOI: 10.3389/fnhum.2022.1074587] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 11/08/2022] [Indexed: 11/25/2022] Open
Abstract
Growing evidences indicate that age plays an important role in the development of mental disorders, but few studies focus on the neuro mechanisms of generalized anxiety disorder (GAD) in different age groups. Therefore, this study attempts to reveal the neurodynamics of Young_GAD (patients with GAD under the age of 50) and Old_GAD (patients with GAD over 50 years old) through statistical analysis of multidimensional electroencephalogram (EEG) features and machine learning models. In this study, 10-min resting-state EEG data were collected from 45 Old_GAD and 33 Young_GAD. And multidimensional EEG features were extracted, including absolute power (AP), fuzzy entropy (FE), and phase-lag-index (PLI), on which comparison and analyses were performed later. The results showed that Old_GAD exhibited higher power spectral density (PSD) value and FE value in beta rhythm compared to theta, alpha1, and alpha2 rhythms, and functional connectivity (FC) also demonstrated significant reorganization of brain function in beta rhythm. In addition, the accuracy of machine learning classification between Old_GAD and Young_GAD was 99.67%, further proving the feasibility of classifying GAD patients by age. The above findings provide an objective basis in the field of EEG for the age-specific diagnosis and treatment of GAD.
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Affiliation(s)
- Jie Wang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
| | - Jiaqi Fang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Engineering, Zhejiang Normal University, Jinhua, China
| | - Yanting Xu
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Engineering, Zhejiang Normal University, Jinhua, China
| | - Hongyang Zhong
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
| | - Jing Li
- College of Foreign Language, Zhejiang Normal University, Jinhua, China
| | - Huayun Li
- College of Teacher Education, Zhejiang Normal University, Jinhua, China,Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China,*Correspondence: Gang Li,
| | - Gang Li
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, China,College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China,Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China,Huayun Li,
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