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2021 Vol. 40, No. 3 Published: 2021-06-20 Reviews Communications Regular Papers        Regular Papers 257 The Difference of Music Processing among Different State of Consciousness: A Study Based on Music Features and EEG Tensor Decomposition Mei Jian, Wang Xiaoyu, Liu Yang, Li Jingqi, Liu Kehong, Yang Yong, Cong Fengyu DOI: 10.3969/j.issn.0258-8021.2021.03.01 In this study, we explored the relationship between different levels of consciousness and music processing. We used the EEG data of 43 participants, including 7 normal participants, 17 minimally conscious state (MCS) participants and 19 vegetative state (VS) participants. During the experiment, participants were requested to listen to the traditional folk music “Jasmine”. We extracted tensor components from the EEG by using hierarchical alternating least squares (HALS) non-negative tensor decomposition. After that, we selected components correlated with the five music informatic features (fluctuation centroid, fluctuation entropy, key clarity, pulse clarity and mode) respectively. Then power spectrum ratio analysis and brain topography analysis were applied to the components selected to study the EEG response. The results showed that: 1) differences existed in the proportion of alpha waves of the EEG response to the 5 music features among the normal group, the MCS group and the VS group: fluctuation centroid (normal group 0.687±0.193, MCS group 0.033±0.022, VS group 0.063±0.040, P<0.001), fluctuation entropy (normal group 0.588±0.132, MCS group 0.041±0.025, VS group 0.085±0.077, P<0.001), key clarity (normal group 0.668±0.295, MCS group 0.096±0.103, VS group 0.057±0.065, P<0.001), pulse clarity (normal group 0.672±0.064, MCS group 0.144±0.242, VS group 0.044±0.044, P<0.001), mode (normal group 0.432±0.273, MCS group 0.057±0.049, VS group 0.033±0.026, P<0.001).Except feature mode, beta wave of the EEG response to the rest 4 music features have the same statistical results as alpha wave (P＜0.05). 2) The rhythm of the response of MCS group and VS group to the 5 music features was mainly the slow wave of delta wave and theta wave. 3) As for the EEG activation area, the normal group EEG response to the 5 music features mainly distributed in frontal lobe but the MCS and VS group EEG response mainlyin parietal lobe. The results showed that the level of consciousness had effects on brain processing music feature, which provided a new research paradigm for exploring the relation between level of consciousness and music processing. 2021 Vol. 40 (3): 257-265 [Abstract] ( 381 ) HTML (1 KB)  PDF (1824 KB)  ( 462 ) 266 Asymmetry of Brain Emotional State Based on EEG and VR Techniques Lan Wenwei, Chen Chen, Zhang Jin, Zhang Jiaqi, Li Feng, Gao Junfeng DOI: 10.3969/j.issn.0258-8021.2021.03.02 In order to distinguish the emotional state of the brain and study the hemispheric asymmetry in different emotional states,20 healthy audiovisual barrier-freesubjects with an average age of 23.9 years were selected as experimental subjects, virtual reality technology and EEG technology were combined to collect EEG signals of subjects under different VR film stimulation, and then the average power spectral density of each frequency band was extracted for uses in the calculation of the index of cerebral hemisphere asymmetry on each frequency band and each brain region. Results show that the asymmetry index of positive emotion and negative emotion in frontal region hemisphere were smaller than that of neutral emotion, whereas in alpha band, the trend of asymmetry index on positive (0.130±0.227), neutral (0.058±0.240) and negative (0.006±0.130) decreased in sequence, indicating that the left frontal region was more active under positive emotion, and there was significant difference between positive emotion and negative emotion group (P < 0.05). While in other bands, the asymmetry index of frontal hemisphere had less difference between the three states. These results provided a new way to distinguish different emotions, and also provided a new method for the judgment and treatment of brain function diseases and psychological diseases. 2021 Vol. 40 (3): 266-271 [Abstract] ( 339 ) HTML (1 KB)  PDF (6770 KB)  ( 318 ) 272 Study on Executive Control Function and Brain Network inHigh Trait Anxiety Individuals Ji Shumei, Bu Xinxin, Xun Xingmiao, Su Xinle, Xu Quansheng DOI: 10.3969/j.issn.0258-8021.2021.03.03 By using behavioral and complex network analysis methods, this study explored the executive control function and brain network characteristics of high trait anxiety (HTA) individuals. Sixteen HTA individuals (as subjects) and sixteen low trait anxiety (LTA) individuals (as controls) were asked to perform Simon spatial conflict task, while behavioral data (response time and accuracy rate) and 64-channel EEG signals were recorded simultaneously. The EEG data were analyzed by synchronous likelihood analysis, and the appropriate threshold value was selected to construct the brain network topology. The overall attribute parameters and node attribute parameters of the network were calculated. The behavior data and the attribute parameters of brain network were analyzed by ANOVA. Results showed that the conflict response time of HTA group was significantly longer than that of LTA group (641.29±72.11 vs 602.10±61.47, P< 0.05), and the response accuracy rate was significantly lower than that of LTA group (90.73±2.14 vs 95.62±1.52, P< 0.05). These results indicated that the efficiency of cognitive conflict response and their executive control ability decreased. Analysis of the brain network in beta rhythm showed that the frontal and parietal node degree of HTA group was significantly less than that of LTA group (P< 0.05), the clustering coefficient (0.5341±0.0813 vs 0.6243±0.0527) and global efficiency (0.0142±0.0037 vs 0.0185±0.0023) was significantly smaller than that of LTA group (P< 0.05), while the characteristic path length was significantly larger than that of LTA group (1.8057±0.0036 vs 1.4380±0.0117, P< 0.05). The results of high gamma rhythmic brain network were like those of beta rhythm. These results suggest that the execution control ability of conflict monitoring and conflict resolution in HTA individuals is reduced. The underlying mechanism is not only related to the impaired function of thefrontal-parietal execution control network, but also related to the weakening of the integration function and information transmission capacity of the brain network. Impaired function offrontal-parietal executive control network and decreased executive control ability are stable and inherent characteristics of HTA individuals. 2021 Vol. 40 (3): 272-279 [Abstract] ( 379 ) HTML (1 KB)  PDF (5978 KB)  ( 407 ) 280 Source Localization Study of Simultaneous EEG-fMRI in Emotion Reappraisal Based on Dipole Feature Optimization Zhang Wei, Jiang Zhongyi, Li Wenjie, Zou Ling DOI: 10.3969/j.issn.0258-8021.2021.03.04 To investigate the source activity of cerebral cortex during emotion reappraisal, a novel fused source localization method based on the dipole feature optimization was proposed for the simultaneous EEG-fMRI data of 15 healthy subjects acquired by the experimental paradigm of emotion reappraisal. First, fMRI-weighted minimum norm estimate was performed. Then 20 ms EEG sliding time window was used to extract dipole spatial fused feature in each time window. A weighted minimum norm estimation was subsequently performed based on the dynamic fused prior. The mechanism comparison of emotion reappraisal was conducted between the results of two source localization methods. Finally, the sample entropy was used to analyze the complexity of EEG source. The experimental results showed that the proposed method could effectively track the dynamics of EEG source on the cerebral cortex and recognized related brain regions during emotion reappraisal task with high temporal and spatial resolutions. In the process of emotion reappraisal, with the emergence of late positive potential in the posterior occipital parietal lobe, the active brain regions transferred from the left inferior parietal lobe, right rostral middle frontal gyrus, left insula to the right superior temporal gyrus and left lateral occipital lobe, and finally activated the right fusiform gyrus, right rostral middle frontal gyrus and right isthmus cingulate gyrus in the late positive potential slow-wave stage (P<0.05). By calculating the sample entropy of EEG source, the significant brain regions were extracted after the subjects received different emotional stimuli within 1500 ms (P<0.05). The active brain region for emotion response was left lateral occipital lobe (negative: 0.688±0.124, neutral: 0.590±0.126). The active brain region for emotion reappraisal was right rostral middle frontal gyms (negative reappraisal: 0.814±0.114, negative: 0.736±0.123). The inhibited brain region for emotion reappraisal was right superior temporal gyrus (negative: 0.642±0.152, negative reappraisal: 0.546±0.090). The obtained results provided the reference of brain regions for the study of cortex EEG source localization related to emotion reappraisal. 2021 Vol. 40 (3): 280-290 [Abstract] ( 314 ) HTML (1 KB)  PDF (6525 KB)  ( 389 ) 291 A Desmoking Algorithm for Endoscopic Images Based on Improved U-Net Lin Jinzhao, Jiang Meiqiu, Pang Yu, Wang Huiqian DOI: 10.3969/j.issn.0258-8021.2021.03.05 In minimally invasive surgery, the smoke generated by operations such as electrocautery and laser ablation seriously fade image quality, not only obstructs the doctor’s field of view and increases the risk of surgery, but also reduces the performance of computer-assisted surgery algorithms (such as segmentation, 3D reconstruction, tracking, etc.). Therefore, the smoke needs to be removed real time to maintain a clear vision. This paper proposed a desmoking algorithm based on the improved U-Net network. To retain more image details, we add images which undergone Laplace pyramid transformation to the encoder part; and to improve network's performance, we add attention mechanism module (CBAM) to decoder part. The laparoscopic image was provided by the Hamlyn Center and used as the original dataset (15000 training images, 1000 synthetic smoke test images, and 129 real smoke test images), and the Blender software was used to simulate various situations of smoke which was added to the laparoscopic image, and the composite image was obtained and sent to the improved U-Net model for training, and performed 5-fold cross-validation. We obtained a PSNR value of 31.05 and SSIM index of 0.98 on the composed dataset. These two indicators showed that the smoke-purified image was very similar to the original image, which helped restore the real vision of the human body during surgery. The average running time is 90.91 fps, which is applicable in a real-time medical system. The obtained results are better than the other six methods based on physical or based on GAN, therefore the proposed approach provided a high-quality solution for the endoscopic smoke removal algorithm, which helps doctors get a clear surgical field of vision. 2021 Vol. 40 (3): 291-300 [Abstract] ( 576 ) HTML (1 KB)  PDF (10645 KB)  ( 339 ) 301 Automatic Location and Classification of Coronary Artery Stenosis Based on Deep Neural Network Cong Chao, Xiao Zhaohui, Chen Wenjun, Wang Yi DOI: 10.3969/j.issn.0258-8021.2021.03.06 In this paper, a deep neural network-based workflow was proposed to automatically detect and classify the stenosis features in coronary images. The algorithm mainly used quantitative coronary angiography (QCA) as a label for supervised learning and classifies the severity of coronary stenosis into normal (< 25% stenosis score) and stenosis (> 25% stenosis) categories and realized stenosis location detection in images. The algorithm used the inception model as the basic classifier to preliminarily classify the image level stenosis, and then combined with the multi-level pool structure to jointly predict the multi perspective angiography image to obtain the left-/right-artery/patient level stenosis prediction. On the basis of the classifier, the feature was further extracted, and the unsupervised learning model was used to realize the narrow location in the image.The training and cross validation were performed on a total of 10872 images in 235 clinical studies. The results showed that the algorithm achieved 85% accuracy and 0.91 AUC score in image level stenosis classification; in multi view joint prediction experiment, the sensitivity and AUC score of 0.94/0.90/0.96 and 0.87/0.88/0.86 respectively for left-/ right-/patient level stenosis classification prediction. In the stenosis localization experiment, the sensitivity of detection for left-/right-artery stenosis was 0.70/0.68, and the mean square error of 512 × 512 image was 37.6/39.3 pixels, respectively. In conclusion, the proposed method realized the potential of auxiliary diagnosis prediction from image to patient with high accuracy, which not only provided the preliminary screening ability in the process of coronary angiography, but also laid the foundation for more accurate and automatic computer-aided diagnosis. 2021 Vol. 40 (3): 301-309 [Abstract] ( 483 ) HTML (1 KB)  PDF (11078 KB)  ( 304 ) 310 Research on Impedance Model of E. coli-NC Membrane Composite Electrode Based on XGBoost Algorithm Xu Ying, Chen Yangzi, Liu Zhe, Sun Lesheng, Jiang Yang, Guo Miao DOI: 10.3969/j.issn.0258-8021.2021.03.07 The purpose of this work is to use machine learning method to analyze the concentration of large quantities of E. coli and evaluate the antibacterial effect of low threshold antibiotics. In this paper, XGBoost machine learning algorithm was used to construct a model of E. coli-NC membrane attachment on gold electrodes, which was used to detect the electrochemical impedance spectrum of E. coli at different concentrations. On this basis, the changes of impedance spectrum corresponding to the action of different concentrations of Amikacin Sulfate antibiotics on the standard concentration of E. coli were analyzed. The impedance curves were fitted with ZView to get seven electrochemical parameters according to the Randles equivalent circuit. The main four parameters of Rs, CPE-P, CPE-T and R1, which were extracted by the principal component analysis (PCA) based on the principle of selecting the top 90% of the information quantity, were input to XGBoost prediction model. Prediction models of the two groups of experiments of E. coli liquid concentration and antibiotic concentration were established based on the predicted value of bacterial fluid concentration and antibiotic concentration. The predicted results of both groups were consistent with the experimental results, with the average root mean square error (RMSE) of the predicted E. coli concentration of 2.18×10-3 lg CFU/mL and the maximum difference between the upper and lower limits of the predicted concentration of each group within 1.49×107 CFU/mL. The average RMSE of antibiotics was 7.45×10-3 μL/mL, and the regression accuracy was 0.01 μL/mL, which realized the rapid and accurate prediction of E. coli and antibiotic concentration. Therefore, the impedance model of E. coli-NC membrane based on XGBoost could be applied to quantitatively analyze the mass concentrations of E. coli and the evaluation of the effect of antibiotic antibacterial at critical low threshold, so as to quantitatively evaluate the long-term impedance change caused by the attachment of micro-proteins, bacteria and other biofilms on the electrode surface, showing great application value and significance in the rapid detection of electrochemistry in the field of food safety. 2021 Vol. 40 (3): 310-320 [Abstract] ( 223 ) HTML (1 KB)  PDF (7700 KB)  ( 128 ) 321 Three-Dimensional Finite Element Analysis of Electroacupuncture for Patients with Knee Osteoarthritis During Ascent and Descent Stair Xu Haifei, Zhao Gaiping, Yang Jiajing, Wang Xiangbin, Xu Shixiong DOI: 10.3969/j.issn.0258-8021.2021.03.08 To explore the availability of finite element analysis method to simulate the biomechanical properties of knee osteoarthritis in the treatment of ascent and descent stair movements, the correlation between the changes of knee biomechanical behavior and the efficacy of electro-acupuncture treatment was compared. Based on image data obtained from CT and MRI, in combination with medical image processing software including Mimics and Geomagic, we established the three-dimensional finite element model of knee flexion 15° downstairs and 50° upstairs. The model included bone and soft tissue structures such as femur and tibia, humerus, medial and lateral meniscus, femoral cartilage, medial and lateral iliac cartilage. The stress distribution of the medial and lateral meniscus, femoral cartilage and medial and lateral tibial cartilage under electroacupuncture treatment were compared by applying the corresponding load on the center of the femoral condyle of the knee joint model of ascent and descent stairs. The research of knee osteoarthritis under electroacupuncture showed that the stress of the medial and lateral meniscus, femoral cartilage, and medial and lateral tibial cartilage was restored to varying degrees after electroacupuncture treatment. The maximum stress at the descent stairs was reduced by 0.543, 0.236, 0.194, 0.239 and 0.327 MPa, respectively. The maximum stress at the ascent stairs was reduced by 0.253, 0.31, 0.227, 0.112 and 0.122 MPa. The stress peak of each cartilage further tends to the normal knee joint, and the stress distribution range was closer to the normal state. The medial cartilage and meniscus of the joint were more loaded than the lateral side, which was consistent with the phenomenon that the clinical medial type of knee osteoarthritis is more than the lateral type. Therefore, it was demonstrated that electroacupuncture treatment could affect the transmission of the force in the knee joint, delay the degradation of cartilage in various parts of the knee joint, and make the stress ratio of each cartilage closer to the normal value. Therefore, the stability of the diseased knee joint was improved. In conclusion, the electroacupuncture treatment could effectively alleviate the stress concentration of articular cartilage in patients with knee osteoarthritis. The biomechanical properties of knee osteoarthritis under electroacupuncture could provide a theoretical basis for the treatment options of clinical knee joints. 2021 Vol. 40 (3): 321-329 [Abstract] ( 298 ) HTML (1 KB)  PDF (9681 KB)  ( 137 )        Reviews 330 Advances in Microneedle-Mediated Therapies for Superficial Tumors Song Gao, Liu Tianqi, Zhang Xueya, Tong Zaizai, Jiang Guohua DOI: 10.3969/j.issn.0258-8021.2021.03.09 As a new emerging approach, microneedle-based transdermal delivery system has the advantages of minimal invasion, painlessness, safety, efficiency, convenient usage and improvement of patient compliance. In the treatment of superficial tumors, microneedles can effectively pierce the stratum corneum of the skin, thereby promoting the loaded-drug to be penetrated and enriched on the tumor sites. The application of microneedles can have the drugs to avoid the clearance by the liver due to the first pass effect, prevent gastrointestinal side effects, and improve the utilization rate, exhibiting better therapeutic effect in comparison with intravenous administration and intratumoral injection under the same drug dose level. Combined with the new therapeutic strategies, including chemotherapy, photothermal therapy, photodynamic therapy, gene therapy and immunotherapy, the research progresses of microneedles for treatment on superficial tumors have been reviewed in this paper, aiming to promote the therapies of superficial tumors with minimally invasive and local precision drug delivery in the future. 2021 Vol. 40 (3): 330-343 [Abstract] ( 270 ) HTML (1 KB)  PDF (8744 KB)  ( 576 ) 344 Research Progress of Biosensor Based on Magnetic Separation Technology Li Dujuan, Feng Shuo, Fan Kai, Liu Hongying, Wang Gaofeng, Su Chang DOI: 10.3969/j.issn.0258-8021.2021.03.10 In order to cope with epidemics, food-borne disease outbreaks and achieve early diagnosis of diseases, people are looking forward to having a fast, sensitive and specific biosensor technology that can play a greater role in the field of biomedical detection. Magnetic separation technology is a molecular biological separation technology that uses the affinity reaction between the surface modification of magnetic beads and the target to complete the separation of the target. It can achieve efficient separation and enrichment of the target. The biosensor combining magnetic separation technology and different detection methods is the key to improve the sensitivity of biosensors in complex backgrounds. This paper reviewed the research progress in this field from the aspects of magnetic nanobeads, magnetic separation methods, biosensor design methods based on magnetic separation technology, and challenges in detecting different targets by using magnetic separation. Research examples have proved the importance of biosensors based on magnetic separation technology in the field of biomedical detection. The review briefly summarized the characteristics of different magnetic separation technologies, as well as the difficulties and challenges faced by biosensors based on magnetic separation technologies and proposed the research trends and directions of biosensors for medical detection. 2021 Vol. 40 (3): 344-353 [Abstract] ( 304 ) HTML (1 KB)  PDF (2202 KB)  ( 641 ) 354 A Review of Brain Tissue Microstructural Imaging Based on Diffusion Magnetic Resonance Xu Yonghong, Ding Ling DOI: 10.3969/j.issn.0258-8021.2021.03.11 Microstructure imaging is a new technology developed to improve some of the shortcomings of traditional diffusion magnetic resonance imaging. The microstructure imaging paradigm aims to estimate and map microscopic properties of tissue using a model that links these properties to the voxel scale MR signal. It is currently undergoing a transition from laboratory research to clinical applications. In this article, we first introduced the diffusion magnetic resonance imaging technology, analyzed the main problems existing and explained the principle of microstructure imaging. In the following parts, the research status of multi-compartment models was reviewed such as CHARMED and NODDI, including model composition, model optimization and clinical application. The research progress of deep learning algorithm applied to microstructure imaging was reviewed as well. At last, the development trend of microstructure imaging technology was prospected. 2021 Vol. 40 (3): 354-363 [Abstract] ( 416 ) HTML (1 KB)  PDF (1766 KB)  ( 805 ) 364 Recent Advances on Antidromic Activation of Deep Brain Stimulation Yi Guosheng, Jiao Lifeng, Wang Jiang, Wei Xile DOI: 10.3969/j.issn.0258-8021.2021.03.12 Deep brain stimulation (DBS) is an effective treatment for movement disorders, which is also a common tool for probing the function of neural circuits. Although the therapeutic efficacy of DBS has already been demonstrated in clinics, the precise mechanisms of its action still remain largely unclear, which limits the further development, optimization, and application of this technology. Electrophysiological experiments and computational models indicate DBS preferentially activates the axons and presynaptic terminals, and the resulting axonal spikes stimulate the cell body and dendrites of target cells as well as the upstream and downstream nuclei through inducing antidromic activation, orthodromic activation, and neurotransmitter release. The fibers projecting to the target nuclei exhibit complex branching structures, which enables the antidromic activation to be an important cellular mechanism underlying the widespread effects of DBS on multiple anatomical structures. Particularly, antidromic activation contributes to the understanding of microscopic, mesoscopic, and macroscopic effects of DBS. In this paper, we first summarized the general cellular principles of action of DBS, and mainly introduced the stimulus-induced antidromic activation. Then, we presented an exhaustive review on the main findings of antidromic activation pattern in recent years, and discussed the implications of antidromic activation for understanding the effects of DBS. Finally, we raised several key issues on the antidromic activation that need to be addressed in the future. 2021 Vol. 40 (3): 364-374 [Abstract] ( 361 ) HTML (1 KB)  PDF (1762 KB)  ( 326 )        Communications 375 Research on Droplet Image Mosaic and Recognition Algorithm Based on Computer Vision Zhang Jiawen, Dong Xiaobin, Miao Guijun, Qiu Xianbo DOI: 10.3969/j.issn.0258-8021.2021.03.13 2021 Vol. 40 (3): 375-379 [Abstract] ( 532 ) HTML (1 KB)  PDF (7510 KB)  ( 213 ) 380 Study of CNV Detection Tools Based on Hidden Markov Model and Whole Exome Sequencing Liu Ni, Liu Han, Zhao Aman, Xu Fanding, Liu Wenyu, Duan Junbo DOI: 10.3969/j.issn.0258-8021.2021.03.14 2021 Vol. 40 (3): 380-384 [Abstract] ( 406 ) HTML (1 KB)  PDF (1949 KB)  ( 523 )

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