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2021 Vol. 40, No. 2 Published: 2021-04-20

Reviews Communications Regular Papers

Regular Papers

	129	Classification of Vibroarthrographic SignalsBased on PCNN-LSTM Neural Network
		Yang Jia, Qiu Tianshuang, Liu Yupeng
		DOI: 10.3969/j.issn.0258-8021.2021.02.01
		The vibroarthrographic (VAG) signal is a sound of the knee joint during flexion and extension. VAG signal can be used to describe the health status of knee joint sensitively and objectively. Hence, it is often used in the detection of knee joint diseases. However, at present, classification accuracy of the normal and abnormal classification method of VAG signal is still low and not automated. The performance needs to be further improved. To solve this problem, in this paper, a classification algorithm of VAG signal based on improved convolutional neural network (PCNN-LSTM) was proposed. First, empirical mode decomposition (EMD) and wavelet transform are used to transform one-dimensional VAG signal into two-dimensional time-frequency characteristic spectrum, which was used as data set. Second, on the basis of CNN, the parallel CNN network structure was combined with LSTM neural network to form the PCNN-LSTM model, which could classify normal or abnormal VAG signals and realizd the automatic detection of knee joint health status. In this paper, the performances of the proposed algorithm were verified by the data set that composed of the real VAG signals collected by the acceleration sensor (181A02) and USB acquisition instrument (FSC812). The data set consisted of 654 samples, including 222 health data and 432 data of patients with knee diseases. Results showed that the classification accuracy of the proposed algorithm was 96.93%, the sensitivity was 100%, and the specificity was 95.56%. Compared with other algorithms, the proposed algorithm achieved better results, and realized the classification and recognition of VAG signals, which was of great significance for non-invasive detection and auxiliary diagnosis of knee joint diseases.
		2021 Vol. 40 (2): 129-136 [Abstract] ( 510 ) HTML (1 KB) PDF (2622 KB) ( 685 )

	137	Effects of High-Frequency Repetitive Transcranial Magnetic Stimulation with Inter-Train Intervals on Power Spectral Density in Bilateral Motor Regions
		Jin Jingna, Liao Wenqing, Liu Wenbo, Wang Xin, Liu Zhipeng, Yin Tao
		DOI: 10.3969/j.issn.0258-8021.2021.02.02
		The effect of inter-train intervals incorporated into high-frequency repetitive transcranial magnetic stimulation (rTMS) on the activity of motor cortex has not been studied adequately yet. The purpose of this study was to investigate the effects of different inter-train intervals (ITI) high-frequency rTMS over primary motor cortex on neural activity energy. Eleven healthy subjects participated in real 10 Hz rTMS with 25, 50 and 100 s ITI and sham 10 Hz rTMS, with trains of 5 s duration. Electroencephalography (EEG) signals with eyes closed in resting state before and after each rTMS session were collected to analyze the changes of power spectral density and its laterality index in total, delta, theta, alpha, beta, gamma 1 and gamma 2 frequency bands in bilateral motor regions. The power spectrum density of stimulated motor cortex in all frequency bands didn’t change significantly after 25 s ITI rTMS (P>0.05). However, the power spectral density of stimulated motor cortex in theta (before vs after (11.42±1.01) dB vs (12.19±1.10) dB) and beta (before vs after (10.71±0.99) dB vs (11.20±0.88) dB) frequency bands increased, and in gamma 2 (before vs after (4.94±0.97) dB vs (3.35±0.61) dB) frequency band decreased significantly after 50 s ITI rTMS (P<0.05). The power spectral density of stimulated motor cortex in theta (before vs after (11.29±1.00) dB vs (12.17±1.10) dB), alpha (before vs after (16.17±1.20) dB vs (17.74±1.20) dB) and beta (before vs after (10.55±0.88) dB vs (11.26±0.90) dB) bands increased significantly after 100 s ITI rTMS. (P<0.05). The changes of power spectrum in non-stimulated motor cortex were similar to that in stimulated motor region. Furthermore, the laterality index of power spectral density in all frequency bands between motor cortex didn’t change after all rTMS (P>0.05). The study demonstrated that the effects of high frequency rTMS with different ITI on the motor cortex activities were different and suggested the ITI should be carefully considered when formulating the high frequency rTMS paradigms.
		2021 Vol. 40 (2): 137-144 [Abstract] ( 350 ) HTML (1 KB) PDF (950 KB) ( 339 )

	145	Study on the Brain Function Network of Transcranial Direct Current Stimulation Intervention on Working Memory
		Lu Sheng, Luo Zhizeng, Shi Hongfei, Gao Yunyuan
		DOI: 10.3969/j.issn.0258-8021.2021.02.03
		Working memory is an important basis of advanced cognitive function. In order to explore the effect of transcranial direct current stimulation on working memory and its specific mechanism, 18 subjects were recruited to participate in the experiment. The behavioral data (accuracy, reaction time) and EEG signals of subjects in the memory load task of three-picture, four-picture and five-picture after stimulation of sham/anode/cathode tDCS were collected. Firstly, the working memory ability was evaluated based on behavioral data. Then, the correlation of EEG signals between different channels was used to construct the brain functional network in each state, and the network characteristic parameters such as average degree(D), average clustering coefficient(C) and global efficiency(E) are calculated. Finally, the changes of behavior data and network characteristic parameters in three kind of memory load tasks after different tDCS stimulation are analyzed. It was shown that compared with sham tDCS(four-picture: 81.25%±2.30%;five- picture: 73.25%±5.36%), the accuracy of the four-picture and five-picture load tasks after anode tDCS (four-picture: 89.75%±1.36%;five- picture: 78.50%±2.25%) were significantly improved (P<0.05), but the behavior data after cathodic tDCS was irregular. According to the change of the characteristic parameters of brain functional network, the height value nodes after anode stimulation were mainly distributed in the stimulation point F3 and the surrounding area. The average degree, average clustering coefficient and global efficiency of the three kinds of memory load tasks were significantly increased compared with those after sham stimulation (P<0.05). The increase of the above three parameters in the four-picture task was the largest (sham tDCS:D=10.55±2.31,C=0.60±0.07,E=0.26±0.03; anodal tDCS: D=15.37±1.35,C=0.71±0.04,E=0.34±0.02). The height value nodes after cathode stimulation were mainly distributed in the right frontal lobe and occipital lobe. Only in the five-picture load task(sham tDCS:D=13.73±2.42, C=0.64±0.07,E=0.31±0.04;cathode tDCS:D=11.46±2.31,C=0.58±0.05,E=0.28±0.03), the average degree, average clustering coefficient and global efficiency are significantly reduced(P<0.05). The results showed that anode tDCS could effectively improve working memory performance by activating the activity of left dorsolateral prefrontal cortex, and the enhancement effect was more significant in the moderate difficulty task; cathode tDCS inhibited the activity of the cortex and reduce the connectivity of the brain, but the brain chose other brain regions for functional compensation, which showed great individual differences, and the inhibition effect was more significant in high difficulty tasks.
		2021 Vol. 40 (2): 145-153 [Abstract] ( 455 ) HTML (1 KB) PDF (3267 KB) ( 374 )

	154	Clinical Application of Resting EEG in Consciousness Diagnosis
		Wang Yong, Liang Zhenhu, Xia Xiaoyu, Bai Yang, Yang Yi, Liu Yangfeng, He Jianghong, Li Xiaoli
		DOI: 10.3969/j.issn.0258-8021.2021.02.04
		The accurate diagnosis of patients with disorder of consciousness (DOC) is of great significance for the treatment plan and outcome, so it is very necessary to develop a reliable method to assess the level of consciousness. In this work, fifty patients diagnosed with DOC (25 vegetable state (VS) patients and 25 minimally consciousness state (MCS) patients) were enrolled. The EEG data were obtained through Bispectral index (BIS) monitor; information complexity and relative power were calculated. The differences of EEG characteristics between MCS and VS groups were analyzed by two sample t-test, and the quantitative relationship between EEG characteristics and clinical scores was analyzed by Pearson correlation analysis. Exploring EEG characteristics to distinguish the states of consciousness. The EEG characteristics were used to build machine-learning model and explore its potential in clinical diagnosis. The results showed that permutation entropy (PE), permutation Lempel-Ziv complexity (PLZC) and the relative power of gamma band were able to distinguish different states of consciousness (PE: 0.71±0.07, 0.75±0.07, P<0.01; PLZC: 0.53±0.07, 0.56±0.06, P<0.01; gamma: 0.13±0.07, 0.16±0.06, P<0.01). PE shows the highest correlation (r=0.81, P<0.05). The area under the ROC curve (AUC) and accuracy (ACC) of consciousness classification model based on PE (AUC=0.931, ACC=0.92) was better than that of BIS (AUC=0.905, ACC=0.90). In conclusion, the resting EEG can be used as an important method for the diagnosis of consciousness.
		2021 Vol. 40 (2): 154-162 [Abstract] ( 328 ) HTML (1 KB) PDF (5097 KB) ( 668 )

	163	Brain Network Topology Study on Precise Grip Force Controlunder Visual Feedback
		Lv Yadong, Li Ke, Hou Ying, Zhang Dongmei, Wei Na
		DOI: 10.3969/j.issn.0258-8021.2021.02.05
		The precise force control of grip is the key to achieve a variety of sophisticated hand functions. When human performs precise force control of grip, whether the speed and the up-down states of force change or not is dominated by different brain functional networks, and the underlying sensorimotor control mechanism remains unclear. The purpose of this study was to investigate the topology changes of the EEG function network related with the speed and up-down states of grip force changes under the vision-precision grip force tracking task. In this study, 11 healthy subjects were recruited. First, the maximum voluntary contraction (MVC) of their grip was measured, and then the subjects were requested to use the thumb and index finger of their right hand to perform vision-precision grip force tracking task at the speed of 1% MVC/s (speed 1), 2% MVC/s (speed 2), and 3% MVC/s (speed 3). The network topology parameters of average clustering coefficient C and the characteristic path length L were used to analyze the EEG functional network based on the phase lag index. The results showed that C values in the θ frequency band during up-ramp state were (0.157±0.032), (0.164±0.044), (0.194±0.039) and during down-ramp state were (0.154±0.026), (0.173±0.041), (0.211±0.058). The C increased significantly in the θ (P<0.05) and similarly in the β (P<0.001) frequency bands. Different from the change of C values, L values in the θ frequency band during up-ramp state were (4.644±0.400), (4.150±0.325), (3.909±0.497) and during down-ramp state were (4.606±0.346), (4.040±0.471), (3.716±0.498). The L decreased significantly in the θ (P<0.001) and similarly in the α, β and γ frequency bands. The local activation of central and posterior parietal were improved with the increasing speed. Except for the L value in the β band [P=0.049] under the condition of tracking speed 2, there was no significant difference between the up and down states. These results indicated that the global and local information transmission efficiency was enhanced with the increasing speed, which meant the brain network connectivity pattern was altered during the adaptation to speed differences. This study provided a basis for exploring the sensorimotor control mechanism of precision grip under the different speeds and up-down states, and provided a new evaluation method for the rehabilitation state of hand function after nervous system diseases.
		2021 Vol. 40 (2): 163-169 [Abstract] ( 330 ) HTML (1 KB) PDF (3686 KB) ( 391 )

	170	Multi-Scale Deep Network for Automatic Sleep Staging
		Bai Haoran, Zhang Wei, Lu Guanze
		DOI: 10.3969/j.issn.0258-8021.2021.02.06
		Sleep quality assessment and diagnosis highly depends on the doctors' effort and experience. It is quite labor intensive and time consuming for the doctor to inspect the long-term sleep monitoring records. The current automatic sleep staging mainly uses traditional machine learning and it highly relies on the features designed by experts. However, these features are usually incapable to capture the deep level features hidden in the measured data, and the behave not well for some staging such as N1. This paper proposed an automatic sleep staging algorithm based on multi-scale deep network. It used the deep network to automatically extract sleep signal features and used multi-scale analysis and discrimination criteria relating to the difficulty measure in the classification of different sleep stages. The classification results of stage W, N2, N3 and REM were selected and output in advance based on shallow layer features, and the fallible transition stage N1 entered a deeper network for further analysis. This policy improved the overall classification efficiency and especially the classification accuracy of the N1 stage. When extracting 197 sets of sample data for training and testing in the Sleep-EDFx data set and using only single-channel EEG signals, the average classification accuracy achieved 83%, and Kappa value was 0.749, which indicated that the constructed models were highly consistent. F1-score at stage N1 achieved 0.51. Compared with traditional machine learning algorithms and a variety of deep networks, the overall classification accuracy and accuracy of the N1 stage were improved. And at the same time, there was no apparent calculation increase. It is suitable for automatic real-time analysis.
		2021 Vol. 40 (2): 170-176 [Abstract] ( 398 ) HTML (1 KB) PDF (2741 KB) ( 548 )

	177	Study on Classification Method of Alzheimer's Disease Convolutional Neural Network Combined with Phenotypic Information
		Li Yuming, He Xuan, Zhu Hongbo, Ge Zhuochen, Zhou Longjie
		DOI: 10.3969/j.issn.0258-8021.2021.02.07
		The early detection and diagnosis of Alzheimer's disease (AD) has important clinical and social significance. Because of the abnormal changes in the topological properties of the functional brain network in AD patients and the large differences in the prevalence of Alzheimer's in different phenotype types, this study combined brain network features and phenotypic information to construct training features for classification at different stages of Alzheimer's disease. In recent years, the graph convolutional neural network (GCN) classification method has proved to be the best choice for graph data learning tasks. Therefore, this paper applied GCN to the classification study of AD and completed for healthy control (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD four types of classification. Herein we used the basic framework of population map convolutional neural network to classify 300 subjects in the ADNI database, and improved methods were proposed in terms of the similarity between the subjects in the population graph and the characteristics of the brain network of the subjects. In terms of the similarity between graph subjects, the construction of other phenotypic graph structures was performed using methods such as addition, improved initial value, only feature similarity, only phenotypic similarity, and four other combination methods; in terms of a brain network feature, combined with multi-modal thinking, the phenotypic information was converted into binary features, and the brain network features were spliced into total features. In addition, this study tried to use different types of phenotype information to the experiments. Finally, 10-fold cross-validation was used to verify the results. The results showed that the improvement in both aspects increased the accuracy to a certain extent. The classification accuracy was the best while using brain network similarity as the edge weight of graph construction, and the phenotypic information (age or gender) without dimension reduction as the characteristics of subjects (nodes). Compared with the original method, the accuracy was improved from 80% to 82%. It was shown that the brain network features and phenotype information were important features in the brain disease classification task, which can help to improve the accuracy of classification task, therefore is of research implications.
		2021 Vol. 40 (2): 177-187 [Abstract] ( 378 ) HTML (1 KB) PDF (5485 KB) ( 949 )

	188	The Role of Cochlear Auditory Pathways on Motor CorticalActivation of Transcranial Magneto-Acoustic Stimulation
		Zhou Xiaoqing, Liu Ruixu, Tan Ruxin, Wang Huiqin, Yin Tao, Liu Zhipeng
		DOI: 10.3969/j.issn.0258-8021.2021.02.08
		Transcranial magneto-acoustic stimulation (TMAS) technique can noninvasively deliver millimeter-scale precision electrical stimulation of the whole brain, including the deep brain regions. Existing experimental results have preliminarily confirmed that TMAS is a composite physical stimulation containing coupled MA electric field and ultrasonic field simultaneously. Recent research results show that cochlear auditory pathway is a necessary condition for cortical activation by focused ultrasound. In this paper, in vivo TMAS and TUS were contrastively stimulated in deaf model mice with damaged auditory pathway for the first time to analyze the role of auditory pathways in the neuromodulation of TMAS. Electromyography (EMG) signals and motor feedback induced by TUS and TMAS were collected from deaf mice (n=6) and normal mice (as the control group, n=6) at the different time points after the deaf-model building. The different neuromodulation effects of two kinds of stimulations on the motor cortex of deaf mice and normal mice were compared and analyzed. Results showed that: At 1 h, 12 h, 24 h and 48 h after model building, the motor feedback of deaf mice under TMAS and TUS gradually disappeared, and the success rate of EMG decreased to 3.33%; Normal mice at the same time point could produce motor feedback and EMG under the two kinds of stimulations, and there was no statistical difference in the success rate of EMG between the TUS group and the TMAS group (p=0.296); but the root mean square amplitude of EMG in the TMAS group was greater than that in the TUS group, and the difference was statistically significant (p=0.0011). The results indicated that the stimulation effects of both TMAS and TUS were heavily dependent on the integrity of auditory pathways. By comparing the results of TUS stimulation between the deaf group and the normal group, it was verified that the existence of auditory pathway was a necessary condition for ultrasound neuromodulation during motor cortex stimulation; the stimulation effect of TMAS was mainly dependent on the stimulation effect of ultrasonic field, and the auditory pathway also had a decisive influence for neuromodulation with TMAS. It is further suggested that the MA electric field in TMAS promote the influence for neuromodulation by TUS in motor cortex stimulation.
		2021 Vol. 40 (2): 188-194 [Abstract] ( 371 ) HTML (1 KB) PDF (1725 KB) ( 427 )

	195	Ultrasound Stimulation on ECoG in Rat Prefrontal Cortex
		Wang Jun, Sui Li, Wu Yongliang, Zheng Zheng
		DOI: 10.3969/j.issn.0258-8021.2021.02.09
		Ultrasound stimulation has neuromodulatory function. In the present study, we explored the neuromodulatory effect and mechanisms of ultrasound stimulation on prefrontal cortex. Ultrasound stimuli were applied transcranially to the prefrontal cortex of anesthetized rats (n=15) and electrocorticogram (ECoG) was recorded before, during (20 min) and after the ultrasound stimulation. The characteristic parameters of ECoG included the total power spectral density (PSD), the PSD of four frequency bands (δ: 0.5～4 Hz, θ: 4～8 Hz, α: 8～13 Hz, β: 13～30 Hz), the average amplitude of ECoG and the average amplitude of the four frequency bands. To attenuate individual differences between experimental rats and reveal the time-effect relationship of ultrasound stimulation, the characteristic parameter values of ECoG during and after ultrasound stimulation were statistically compared and analyzed in the form of percentages relative to pre-stimulation (defined as 100%). The results could be described from four aspects. 1) The whole ultrasound stimulation process was divided into 7 time periods, which were 0~1, 1~2, 2~3, 3~4, 4~5, 5~10,10~20 min of ultrasound stimulation, respectively. The total PSD of ECoG was 139.2±13.2% in prefrontal cortex within 1 min of the applied ultrasound stimulation, and the difference was significant compared with that before the stimulation (P<0.05). The differences between the total PSD of ECoG in the other 6 time periods compared to pre-stimulation were all significant (P<0.05). Within 5 min after the cessation of ultrasound stimulation, the PSD of ECoG was 90.1±9.9% of that before stimulation, and the difference was not significant compared with that before stimulation (P>0.05).2) Ultrasound stimulation had an enhancing effect on PSD in all four frequency bands, and the difference between PSD in all four frequency bands in ultrasound stimulation and before stimulation was significant (P<0.05). After the cessation of ultrasound stimulation, there was no significant difference between the PSD of the four frequency bands compared with that before stimulation (P>0.05). 3) Mean amplitude of ECoG: Thechange of mean amplitude of ECoG in ultrasound stimulationwas significant compared with that before stimulation (P<0.05); After ultrasound stimulation was stopped, the difference was not significant compared with that before stimulation (P>0.05). 4) For the mean amplitude of the four frequency bands, ultrasound stimulation could enhance the mean amplitude of the four frequency bands. Furthermore, this enhancement was manifested in the δ band up to 10 min, and the enhancement in the θ, α and β bands was less than 5 min. Ultrasound stimulation regulated ECoG in rat prefrontal cortex and this regulation has the characteristics of promptness and rapid recovery.
		2021 Vol. 40 (2): 195-201 [Abstract] ( 312 ) HTML (1 KB) PDF (1755 KB) ( 303 )

	202	Study on the Screening and Diagnosis of Oral Diseases Based on Volatile Sulfur Compounds in Human Exhaled Breath
		Xue Yingying, Zhang Tao, Yang Tingting, Chen Yuantao, Zhang Junyu, Wan Hao, Ye Wei, Wang Ping
		DOI: 10.3969/j.issn.0258-8021.2021.02.10
		This study established a standardized method and procedure to detect volatile sulfide compounds (VSCs) in human exhaled breath by gas chromatography and mass spectrometry (GC-MS) for the first time. By this way, 65 breath samples were collected using airbags, and the concentrations of VSCs were analyzed by GC-MS and instruments currently used in clinical applications, the correlation of which were analyzed. Further, receiver operating characteristic curve (ROC) method was adopted to establish the threshold model of halitosis grade, and the periodontal disease diagnosis models based on linear discriminant analysis, logistic regression, and support vector machine were designed. The results of GC-MS method were correlated with the breath detection instruments (P<0.01). The sensitivity of halitosis grade threshold diagnostic model based on ROC reached 0.897 at present. The sensitivity and specificity of the periodontal disease diagnosis model were 0.733 and 0.771 respectively by logistic regression method. This work validated the feasibility to screen and diagnose oral diseases based on VSCs with good performance. It could be predicted that the combination with other markers of exhaled gas would provide a more simple, non-invasive and rapid diagnostic way for disease diagnosis.
		2021 Vol. 40 (2): 202-209 [Abstract] ( 314 ) HTML (1 KB) PDF (3056 KB) ( 688 )

	210	A Tumor Targeted Controlled-Release Nano-CuS Loaded Erythrocytes for Tumor Photothermal Therapy
		Chen Chao, Wang Xianger, Qiu Yun, Huang Hao, Wang Yufei, Xia Donglin, Gu Haiying
		DOI: 10.3969/j.issn.0258-8021.2021.02.11
		The application of photothermal agent copper sulfide nanoparticles (CuS) requires an increase in the drug concentration to achieve desired therapeutic effect. However, the increase of drug concentration would lead to more side effects, which limited clinical applications. In this study, erythrocytes were used as the carriers for CuS, which can rapidly respond to the laser, and tumor targeted release of CuS can increase photothermal effects. In this work, 40 mice bearing tumor were randomly divided into 4 groups, control group, laser treated group, CuS+laser group, CuS@ER+laser group, there were 10 mice in each group. According to the modified expansion method, the nano CuS was packed into erythrocytes, and the laser-responsive release behavior was investigated after lasered. The target behavior was examined with a in vivo imaging detection system for small animals and the photothermal therapy effect was detected by thermal imager. Additional outcome measures included tumor response rate, overall survival, and safety. It was found out that CuS was loaded into erythrocytes and the loading efficiency was 17.24%±0.98%. The biocompatibility of the CuS was improved due to the encapsulation of erythrocytes and the photothermal conversion efficiency was over 53% after 0.44 W/cm²980 nm laser radiation. The concentration of CuS in the tumor after CuS@ER treatment was(0.061±0.007)μg/g, it was significantly higher than that of the free nano-CuS treatment (P<0.01), because of laser-response release behavior. The inhibitory rate of tumor growth for the CuS@ER group was 0.91 ± 0.02 with less side effects, as the temperature of the photothermal therapy increased. The survival rate of CuS@ER treatment was 90% after 48 days with less side effects. Our study provided a promising strategy to increased photothermal therapy without concede biocompatibility, by using an erythrocyte-inspired and laser-activatable platform.
		2021 Vol. 40 (2): 210-217 [Abstract] ( 398 ) HTML (1 KB) PDF (11378 KB) ( 76 )

Reviews

	218	EEG Neurofeedback for Cognitive Rehabilitation in Depression: Progress and Challenges
		Li Yutong, Wang Yinxue, Huang Gan, Liang Zhen, Zhang Li, Zhang Zhiguo, Li Linling
		DOI: 10.3969/j.issn.0258-8021.2021.02.12
		Depression is a common and acute mental disease and cognitive impairment is one of the core symptoms of depression, which greatly affects the daily life of patients and imposes a heavy burden on the family and the society. However, effective rehabilitation methods for cognitive impairment in depression are still lacking, the relevant neural mechanism remains unclear, and therapeutic effects vary greatly from individual to individual. Electroencephalogram (EEG) neurofeedback has attracted more and more attention because it is safe, non-invasive, and without side effects. This paper reviewed the EEG features of cognitive impairment in depression, introduced existing EEG neurofeedback training studies based on these features, and discussed the current problems and future development. With the rapid development of neurofeedback techniques and a deep understanding of the underlying mechanisms, EEG neurofeedback has the potential to be a useful tool in cognitive rehabilitation of depression.
		2021 Vol. 40 (2): 218-227 [Abstract] ( 350 ) HTML (1 KB) PDF (830 KB) ( 885 )

	228	Research Progress of Motor Imagery Combined with Occupational Therapy in Rehabilitation Training of Stroke Patients
		Zhao Xin, Wu Haixia, Chen Long, Wang Zhongpeng, Gu Bin, Ming Dong
		DOI: 10.3969/j.issn.0258-8021.2021.02.13
		As traditional rehabilitation methods,motor imagery(MI) and occupational therapy(OT) have been widely used in the clinical rehabilitation of stroke patients. This paper first briefly described the basic principles of OT and MI in stroke rehabilitation training, and the specifically reviewed the relevant research progress in the clinical application of OT and MI in the treatment of different functional disorders such as motor function and cognitive function in stroke rehabilitation. Among them, MI has the advantages of being simple and easy to implement, low cost, and can fully mobilize the subjective initiative of patients. However, it has the disadvantages of being single in form and difficult to control the process. The OT experiment paradigm is simple and has fixed mode guidance, with numerous actions and fits in with daily life, which can add situational and task-oriented training mode to MI. Finally, the research progress of MI combined with OT in stroke rehabilitation training was reviewed. The study showed that the combination of MI and OT could give play to the advantages of both, and is expected to further improve the rehabilitation process of patients. In the future, it will be continuously optimized and improved in terms of training methods, neural mechanism and system classification performance.
		2021 Vol. 40 (2): 228-236 [Abstract] ( 543 ) HTML (1 KB) PDF (818 KB) ( 671 )

	237	Advances in the Development of Liver Organoids
		Jing Sijia, Zheng Huilin, Zhang Lei
		DOI: 10.3969/j.issn.0258-8021.2021.01.014
		The main purpose of liver pathology research is to study the pathogenesis and drug development of fatty liver, hepatitis, liver fibrosis and liver cancer. At the same time, liver disease is complicated and difficult to treat and shows a tendency of disease progression. The development from fatty liver to cirrhosis and then to liver cancer requires a whole-process research model. At present, liver diseases are mainly studied through two-dimensional cell culture in vitro and animal models in vivo, but neither can fully reveal the pathogenesis of liver diseases and the effect of drug treatment. The development of liver organoids through three-dimensional culture technology makes the further study of liver diseases possible. In this paper, the advances in the field of liver organoids and their applications in biomedicine were reviewed. The key technologies including the selection of seed cells and the construction method of liver organoids were summarized, and their practical applications in building disease models, screening drugs and the detection of hepatotoxicity were introduced, which proved the importance of liver organoids.
		2021 Vol. 40 (2): 237-246 [Abstract] ( 379 ) HTML (1 KB) PDF (2456 KB) ( 1250 )

Communications

	247	Effects of Low-High Frequency Combination Repetitive Transcranial Magnetic Stimulation on Dysphagia and the Electromyographic Activity of the Associated Muscle Group in Elderly Cerebral Infarction Patients
		Lv Mingxin, Liu Shuangjie, Wang Yuqin, Liang Junjun, Li Tingting
		DOI: 10.3969/j.issn.0258-8021.2021.02.15

		2021 Vol. 40 (2): 247-251 [Abstract] ( 263 ) HTML (1 KB) PDF (717 KB) ( 196 )

	252	Construction of an Evaluation Model for High Arch Anomaly Based on BP Neural Network
		Wang Xinting, Wang Qi, Xu Dandi, Qiu Nian, Ren Jianping
		DOI: 10.3969/j.issn.0258-8021.2021.02.16

		2021 Vol. 40 (2): 252-256 [Abstract] ( 241 ) HTML (1 KB) PDF (1078 KB) ( 230 )

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