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Application and exploration of fNIRS in brain function mechanism of acupuncture against insomnia: fNIRS 认领 引用
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作者 Yu-juan YE Bao-jin WANG +1 位作者 LI CHEN Xing-ke YAN 《World Journal of Acupuncture-Moxibustion》 CAS CSCD 2026年第1期84-88,共5页
Functional near-infrared spectroscopy (fNIRS) is predominantly employed to investigate the atypical response characteristics of the cerebral cortex in neurological disorders. Recently, this technology has been applied... Functional near-infrared spectroscopy (fNIRS) is predominantly employed to investigate the atypical response characteristics of the cerebral cortex in neurological disorders. Recently, this technology has been applied to examine the neural mechanisms underlying the use of acupuncture in the treatment of insomnia. This article provides a comprehensive review and analysis of the current applications of fNIRS in the contexts of sleep-wake cycles, insomnia, and acupuncture-based interventions for insomnia. The findings indicate that existing research predominantly focuses on the prefrontal cortex, with limited studies addressing the temporal, occipital, and parietal lobes. fNIRS can be integrated with various data analysis methodologies or multimodal brain functional imaging techniques to investigate the regulatory effects of acupuncture on functional connectivity both among and within brain regions, as well as on the efficiency of information transmission. This approach may elucidate the neural mechanisms of acupuncture in combating insomnia from diverse perspectives and offer an enhanced framework for the diagnosis and treatment of insomnia. 展开更多
关键词 fNIRS Acupuncture Insomnia Mechanisms of brain function
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A study of connectivity features analysis in brain function network for dementia recognition 认领 引用
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作者 Siying Li Peng Wang +6 位作者 Zhenfeng Li Lidong Du Xianxiang Chen Jie Sun Libin Jiang Gang Cheng Zhen Fang 《Nanotechnology and Precision Engineering》 EI CAS CSCD 2025年第1期79-93,共15页
Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuro... Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuronal damage,it is crucial to find a biomarker to distinguish individuals with these diseases from healthy people.In this study,we construct a brain function network based on electroencephalography data to study changes in AD and MCI patients.Using a graph-theoretical approach,we examine connectivity features and explore their contributions to dementia recognition at edge,node,and network levels.We find that connectivity is reduced in AD and MCI patients compared with healthy controls.We also find that the edge-level features give the best performance when machine learning models are used to recognize dementia.The results of feature selection identify the top 50 ranked edge-level features constituting an optimal subset,which is mainly connected with the frontal nodes.A threshold analysis reveals that the performance of edge-level features is more sensitive to the threshold for the connection strength than that of node-and network-level features.In addition,edge-level features with a threshold of 0 provide the most effective dementia recognition.The K-nearest neighbors(KNN)machine learning model achieves the highest accuracy of 0.978 with the optimal subset when the threshold is 0.Visualization of edge-level features suggests that there are more long connections linking the frontal region with the occipital and parietal regions in AD and MCI patients compared with healthy controls.Our codes are publicly available at http://gffzz188fe103f8f1460asxqupn600nupn6kfw.ffgz.tsg.suse.edu.cn/Debbie-85/eeg-connectivity. 展开更多
关键词 Electroencephalography Brain function network Machine learning Feature selection Dementia recognition
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Near-infrared brain functional characteristics of mild cognitive impairment with sleep disorders 认领 引用 被引量：2
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作者 Heng Liao Sha Liao +5 位作者 Yu-Jiao Gao Xi Wang Li-Hong Guo Su Zheng Wu Yang Yi-Nan Dai 《World Journal of Psychiatry》 SCIE 2025年第1期106-116,共11页
BACKGROUND Mild cognitive impairment(MCI)has a high risk of progression to Alzheimer’s disease.The disease is often accompanied by sleep disorders,and whether sleep disorders have an effect on brain function in patie... BACKGROUND Mild cognitive impairment(MCI)has a high risk of progression to Alzheimer’s disease.The disease is often accompanied by sleep disorders,and whether sleep disorders have an effect on brain function in patients with MCI is unclear.AIM To explore the near-infrared brain function characteristics of MCI with sleep disorders.METHODS A total of 120 patients with MCI(MCI group)and 50 healthy subjects(control group)were selected.All subjects underwent the functional near-infrared spec-troscopy test.Collect baseline data,Mini-Mental State Examination,Montreal Cognitive Assessment scale,fatigue severity scale(FSS)score,sleep parameter,and oxyhemoglobin(Oxy-Hb)concentration and peak time of functional near-infrared spectroscopy test during the task period.The relationship between Oxy-RESULTS Compared with the control group,the FSS score of the MCI group was higher(t=11.310),and the scores of Pittsburgh sleep quality index,sleep time,sleep efficiency,nocturnal sleep disturbance,and daytime dysfunction were higher(Z=-10.518,-10.368,-9.035,-10.661,-10.088).Subjective sleep quality and total sleep time scores were lower(Z=-11.592,-9.924).The sleep efficiency of the MCI group was lower,and the awakening frequency,rem sleep latency period,total sleep time,and oxygen desaturation index were higher(t=5.969,5.829,2.887,3.003,5.937).The Oxy-Hb concentration at T0,T1,and T2 in the MCI group was lower(t=14.940,11.280,5.721),and the peak time was higher(t=18.800,13.350,9.827).In MCI patients,the concentration of Oxy-Hb during T0 was negatively correlated with the scores of Pittsburgh sleep quality index,sleep time,total sleep time,and sleep efficiency(r=-0.611,-0.388,-0.563,-0.356).It was positively correlated with sleep efficiency and total sleep time(r=0.754,0.650),and negatively correlated with oxygen desaturation index(r=-0.561)and FSS score(r=-0.526).All comparisons were P<0.05.CONCLUSION Patients with MCI and sleep disorders have lower near-infrared brain function than normal people,which is related to sleep quality.Clinically,a comprehensive assessment of the near-infrared brain function of patients should be carried out to guide targeted treatment and improve curative effect. 展开更多
关键词 Mild Cognitive impairment Sleep disorders Near-infrared Brain functional Characteristics
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Brain Functional Network Changes in Patients with Poststroke Cognitive Impairment Following Acupuncture Therapy 认领 引用 被引量：2
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作者 Ran Wang Nian Liu +4 位作者 Hao Xu Peng Zhang Xiaohua Huang Lin Yang Xiaoming Zhang 《Health》 2024年第9期856-871,共16页
Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture t... Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture therapy. Methods: Twenty-two PSCI patients who underwent acupuncture therapy in our hospital were enrolled as research subjects. Another 14 people matched for age, sex, and education level were included in the normal control (HC) group. All the subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans;the PSCI patients underwent one scan before acupuncture therapy and another after. The network metric difference between PSCI patients and HCs was analyzed via the independent-sample t test, whereas the paired-sample t test was employed to analyze the network metric changes in PSCI patients before vs. after treatment. Results: Small-world network attributes were observed in both groups for sparsities between 0.1 and 0.28. Compared with the HC group, the PSCI group presented significantly lower values for the global topological properties (γ, Cp, and Eloc) of the brain;significantly greater values for the nodal attributes of betweenness centrality in the CUN. L and the HES. R, degree centrality in the SFGdor. L, PCG. L, IPL. L, and HES. R, and nodal local efficiency in the ORBsup. R, ORBsupmed. R, DCG. L, SMG. R, and TPOsup. L;and decreased degree centrality in the MFG. R, IFGoperc. R, and SOG. R. After treatment, PSCI patients presented increased degree centrality in the LING.L, LING.R, and IOG. L and nodal local efficiency in PHG. L, IOG. R, FFG. L, and the HES. L, and decreased betweenness centrality in the PCG. L and CUN. L, degree centrality in the ORBsupmed. R, and nodal local efficiency in ANG. R. Conclusion: Cognitive decline in PSCI patients may be related to BFN disorders;acupuncture therapy may modulate the topological properties of the BFNs of PSCI patients. 展开更多
关键词 Cognitive Decline Poststroke Cognitive Impairment Functional Magnetic Resonance Imaging Brain Functional Network Graph Theoretical Analysis
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Effect of brain functional recovery decoction on expression of vascular endothelial growth factor and Ang-1 protein in a rat cerebral ischemia reperfusion model 认领 引用 被引量：1
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作者 Yang Baocun Zhang Caixia +2 位作者 Yang Le Yang Jing Xiao Gaiqin 《Journal of Traditional Chinese Medicine》 SCIE CSCD 2017年第1期76-79,共4页
OBJECTIVE: To investigate the effect of brain functional recovery decoction(BFRD) on expression of vascular endothelial growth factor(VEGF) and angiopoietin-1(Ang-1) protein in rats with cerebral ischemia reperfusion ... OBJECTIVE: To investigate the effect of brain functional recovery decoction(BFRD) on expression of vascular endothelial growth factor(VEGF) and angiopoietin-1(Ang-1) protein in rats with cerebral ischemia reperfusion injury, and to explore the mechanism of action of BFRD.METHODS: Using the suture-occlusion method, a Wistar rat model of focal cerebral ischemia reperfusion was established. The rats were randomly divided into treatment group, model group, and sham operation group. The treatment group was administered BFRD. In situ hybridization was used to detect VEGF m RNA expression. Immunohistochemistry was used to observe expression of Ang-1 protein.RESULTS: VEGF mRNA expression was greater in the model group compared with the sham operation group(P < 0.05); Ang-1 protein expression was more obvious in the treatment group than the model group(P < 0.05).CONCLUSION: BFRD promoted VEGF m RNA and Ang-1 protein expression in the brains of rats with cerebral ischemia, suggesting increased angiogenesis. 展开更多
关键词 Vascular endothelial growth factors Brain ischemia Angiopoietin-1 Stroke Brain functional recovery decoction
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Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization 认领 引用 被引量：1
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作者 Zhuqing Jiao Yixin Ji +1 位作者 Tingxuan Jiao Shuihua Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第5期845-871,共27页
Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the di... Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes. 展开更多
关键词 Brain functional network sub-network functional connectivity graph regularized nonnegative matrix factorization(GNMF) aggregation matrix
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Effects of exercise on brain functions in diabetic animal models 认领 引用 被引量：1
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作者 Sun Shin Yi 《World Journal of Diabetes》 2015年第4期583-597,共15页
Human life span has dramatically increased over several decades,and the quality of life has been considered to be equally important.However,diabetes mellitus(DM)characterized by problems related to insulin secretion a... Human life span has dramatically increased over several decades,and the quality of life has been considered to be equally important.However,diabetes mellitus(DM)characterized by problems related to insulin secretion and recognition has become a serious health problem in recent years that threatens human health by causing decline in brain functions and finally leading to neurodegenerative diseases.Exercise is recognized as an effective therapy for DM without medication administration.Exercise studiesusing experimental animals are a suitable option to overcome this drawback,and animal studies have improved continuously according to the needs of the experimenters.Since brain health is the most significant factor in human life,it is very important to assess brain functions according to the different exercise conditions using experimental animal models.Generally,there are two types of DM;insulin-dependent type 1 DM and an insulin-independent type 2 DM(T2DM);however,the author will mostly discuss brain functions in T2 DM animal models in this review.Additionally,many physiopathologic alterations are caused in the brain by DM such as increased adiposity,inflammation,hormonal dysregulation,uncontrolled hyperphagia,insulin and leptin resistance,and dysregulation of neurotransmitters and declined neurogenesis in the hippocampus and we describe how exercise corrects these alterations in animal models.The results of changes in the brain environment differ according to voluntary,involuntary running exercises and resistance exercise,and gender in the animal studies.These factors have been mentioned in this review,and this review will be a good reference for studying how exercise can be used with therapy for treating DM. 展开更多
关键词 Diabetes mellitus Involuntary and voluntary exercise Resistance exercise Brain function Animal models
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Research on Modeling Approach of Brain Function Network Based on Anatomical Distance 认领 引用
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作者 杨艳丽 郭浩 +1 位作者 陈俊杰 李海芳 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第6期758-762,共5页
The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function netw... The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging(f MRI)network. Finally, the result shows that the best model only is based on anatomical distance. 展开更多
关键词 resting-state brain function network model network connection distance minimization topological property anatomical distance common neighbor
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Brain Functional Network Improved by Magnetic Stimulation at Acupoints during Mental Fatigue 认领 引用
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作者 Shuo Yang Na Ai +3 位作者 Yanyun Qiao Lei Wang Hongli Yu Guizhi Xu 《Journal of Biomedical Science and Engineering》 2016年第10期65-70,共6页
To investigate the effects of magnetic stimulation at acupoints on brain functional network during mental fatigue, magnetic stimulation was applied to stimulate SHENMEN (HT7), HEGU (LI4) and LAOGONG (PC8) acupoint in ... To investigate the effects of magnetic stimulation at acupoints on brain functional network during mental fatigue, magnetic stimulation was applied to stimulate SHENMEN (HT7), HEGU (LI4) and LAOGONG (PC8) acupoint in this paper. The brain functional networks of normal state, mental fatigue state and stimulated state were constructed and the characteristic parameters were comparatively studied based on the complex network theory. The results showed that the connection of the network was enhanced by stimulating the HT7, LI4 and PC8 acupoint. In conclusion, magnetic stimulation at acupoints can effectively relieve mental fatigue. 展开更多
关键词 Magnetic Stimulation Acupoint Electroencephalograph (EEG) Mental Fatigue Brain Functional Network
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Brain Functional Network Based on Small-Worldness and Minimum Spanning Tree for Depression Analysis 认领 引用 被引量：1
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作者 Bingtao Zhang Dan Wei +1 位作者 Yun Su Zhonglin Zhang 《Journal of Beijing Institute of Technology》 EI CAS 2023年第2期198-208,共11页
Since the outbreak and spread of corona virus disease 2019(COVID-19),the prevalence of mental disorders,such as depression,has continued to increase.To explore the abnormal changes of brain functional connections in p... Since the outbreak and spread of corona virus disease 2019(COVID-19),the prevalence of mental disorders,such as depression,has continued to increase.To explore the abnormal changes of brain functional connections in patients with depression,this paper proposes a depression analysis method based on brain function network(BFN).To avoid the volume conductor effect,BFN was constructed based on phase lag index(PLI).Then the indicators closely related to depression were selected from weighted BFN based on small-worldness(SW)characteristics and binarization BFN based on the minimum spanning tree(MST).Differences analysis between groups and correlation analysis between these indicators and diagnostic indicators were performed in turn.The resting state electroencephalogram(EEG)data of 24 patients with depression and 29 healthy controls(HC)was used to verify our proposed method.The results showed that compared with HC,the information processing of BFN in patients with depression decreased,and BFN showed a trend of randomization. 展开更多
关键词 depression brain function network(BFN) small-worldness(SW) minimum spanning tree(MST)
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Evaluation of Prognosis of Brain Function with Early Transcranial Color Doppler Ultrasound in Patients after Cardiopulmonary Resuscitation 认领 引用 被引量：1
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作者 Hui Guo Zhangshun Shen +4 位作者 Ning Xu Qian Zhao Hongling Li Yangjuan Jia Jianguo Li 《World Journal of Cardiovascular Diseases》 CAS 2020年第9期658-665,共8页
Objective: To evaluate the clinical value of transcranial color Doppler ultrasound (TCCD) in assessing cerebral function after cardiopulmonary resuscitation (CPR). Methods: A prospective study was conducted in 52 pati... Objective: To evaluate the clinical value of transcranial color Doppler ultrasound (TCCD) in assessing cerebral function after cardiopulmonary resuscitation (CPR). Methods: A prospective study was conducted in 52 patients with cardiac arrest treated by CPR from January 2018 to January 2020, and its clinical data were analyzed. According to classification of cerebral performance category (CPC), 31 cases (CPC grade 1 - 2) were selected in the good prognosis group and 21 cases (CPC grade 3 - 5) in the poor prognosis group. The cerebral blood flow was measured by transcranial Doppler ultrasound (TCCD) 24 h after CPR, and the differences were compared between the two groups in stroke index, diastolic blood flow velocity (Vd), systolic peak blood flow velocity (Vs) and mean peak blood flow velocity (Vm). The ROC curve of cerebral blood flow after CPR was drawn to predict the prognosis of brain function. Results: The data showed that the pulsatility index of middle cerebral artery of the poor prognosis group decreased within 24 h;the difference between the two groups was statistically significant (p ;the difference between the two groups was statistically significant (p Conclusion: The cerebral blood flow increase in the early stage of successful CPR is positively correlated with the prognosis of cerebral functional resuscitation. Monitoring intracranial blood flow after CPR by TCCD has clinical value to evaluate prognosis of brain function. 展开更多
关键词 Cardiopulmonary Resuscitation (CPR) Transcranial Color Bifunctional Ultrasound (TCCD) Cerebral Blood Flow Prognosis of Brain Function
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Homeostatic regulation of brain functions by endocannabinoid signaling 认领 引用 被引量：1
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作者 Chu Chen 《Neural Regeneration Research》 SCIE CAS CSCD 2015年第5期691-692,共2页
Humans have been using Cannabis and its extracts for a few thousand years as a medicinal and recreational drug. How- ever, the chemical component in Cannabis sativa, △9-tet- rahydrocannabinol （△9-THC）, an exogenou... Humans have been using Cannabis and its extracts for a few thousand years as a medicinal and recreational drug. How- ever, the chemical component in Cannabis sativa, △9-tet- rahydrocannabinol （△9-THC）, an exogenous cannabinoid, remained unknown until it was isolated and identified as the main psychoactive ingredient （Gaoni and Mechoulam, 1964）. 展开更多
关键词 CB Homeostatic regulation of brain functions by endocannabinoid signaling
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Assessing target optical camouflage effects using brain functional networks:A feasibility study 认领 引用
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作者 Zhou Yu Li Xue +4 位作者 Weidong Xu Jun Liu Qi Jia Jianghua Hu Jidong Wu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第4期69-77,共9页
Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby c... Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy. 展开更多
关键词 Camouflage effect evaluation Electroencephalography(EEG) Brain functional networks Machine learning
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Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment 认领 引用
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作者 Zhengtao Xi Chaofan Song +2 位作者 Jiahui Zheng Haifeng Shi Zhuqing Jiao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第6期2243-2266,共24页
The structure and function of brain networks have been altered in patients with end-stage renal disease(ESRD).Manifold regularization(MR)only considers the pairing relationship between two brain regions and cannot rep... The structure and function of brain networks have been altered in patients with end-stage renal disease(ESRD).Manifold regularization(MR)only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions.To solve this issue,we developed a method to construct a dynamic brain functional network(DBFN)based on dynamic hypergraph MR(DHMR)and applied it to the classification of ESRD associated with mild cognitive impairment(ESRDaMCI).The construction of DBFN with Pearson’s correlation(PC)was transformed into an optimization model.Node convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure,and then got the dynamic hypergraph to form the manifold regular terms of the dynamic hypergraph.The DHMR and L1 norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN(DDBFN).Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction methods.Our work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI,providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments. 展开更多
关键词 End-stage renal disease mild cognitive impairment brain functional network dynamic hypergraph manifold regularization classification
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Optical recording of brain functions based on voltage-sensitive dyes 认领 引用
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作者 Qian Yu Xing Wang Liming Nie 《Chinese Chemical Letters》 SCIE CAS CSCD 2021年第6期1879-1887,共9页
To better understand the spatial distribution of brain functions,we need to monitor and analyze neuronal activities.Electrophysiological technique has provided an important method for the exploration of some neural ci... To better understand the spatial distribution of brain functions,we need to monitor and analyze neuronal activities.Electrophysiological technique has provided an important method for the exploration of some neural circuits.However,this method cannot simultaneously detect the activities of nerve cell groups.Therefore,methods that can monitor the spatial distribution of neuronal population activity are demanded to explore brain functions.Voltage-sensitive dyes(VSDs)shift their absorption or emission optical signals in response to different membrane potentials,allowing assessing the global electrical state of neurons.Optical recording technique coupled with VSDs is a promising method to monitor the brain functions by detecting optical signal changes.This review focuses on the fast and slow responses of VSDs to membrane potential changes and optical recordings utilized in the central nervous system.In this review,we attempt to show how VSDs and optical recordings can be used to obtain brain functional monitoring at high spatial and temporal resolution.Understanding of brain functions will not only greatly improve the cognition of information transmission of complex neural network,but also provide new methods of treating brain diseases such as Parkinson’s and Alzheimer’s diseases. 展开更多
关键词 Voltage-sensitive dyes Cyanine dyes Membrane potential Brain function Photoacoustic imaging
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Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis 认领 引用 被引量：2
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作者 Qiankun Zuo Junhua Hu +5 位作者 Yudong Zhang Junren Pan Changhong Jing Xuhang Chen Xiaobo Meng Jin Hong 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第12期2129-2147,共19页
The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlat... The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders.However,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia diagnosis.In this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven models.Specifically,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation robust.Also,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions.The typical topological properties and discriminative features can be preserved entirely.Furthermore,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive diseases.Attempts on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional networks.The classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related augmentedmodels.Overall,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks. 展开更多
关键词 Adversarial graph encoder label distribution generative transformer functional brain connectivity graph convolutional network dementia
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Changes in brain functional network connectivity after stroke 认领 引用 被引量：5
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作者 Wei Li Yapeng Li +1 位作者 Wenzhen Zhu Xi Chen 《Neural Regeneration Research》 SCIE CAS CSCD 2014年第1期51-60,共10页
Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore func... Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke. 展开更多
关键词 nerve regeneration brain injury stroke motor areas functional magnetic resonanceimaging brain network independent component analysis functional network connectivity neuralplasticity NSFC grant neural regeneration
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Anxiety Network of Brain Function in Patients with Acute Cerebral Infarction 认领 引用 被引量：1
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作者 Huanyin Li Huiwen Gui +1 位作者 Ye Yao Jixian Lin 《Health》 2021年第7期777-787,共11页
Objective: To explore the characteristics of brain functional network with anxiety in patients with acute cerebral infarction. Methods: A total of 39 patients with acute cerebral infarction by cranial magnetic resonan... Objective: To explore the characteristics of brain functional network with anxiety in patients with acute cerebral infarction. Methods: A total of 39 patients with acute cerebral infarction by cranial magnetic resonance examination were included, and all the patients were scored by the Hamilton Anxiety Scale. The anxiety scale is scored by a professional psychiatrist. There are a total of 14 items, including anxiety, nervousness, fear, insomnia, cognitive function, depressed mood, somatic anxiety, sensory system, etc. The total score ≥ 29 points may be severe;≥21 points, there must be obvious;≥14 points, there must be anxiety;a score of more than 7 may indicate anxiety. If the score is less than 7, there are no anxiety symptoms. All patients within 24 to 72 hours, complete the head examination magnetic resonance, computerized calculation of the DWI sequence images, according to the results of the calculation to superimpose the image of the lesion, image reconstruction in space, and carry out Binarization, defining the value of lesions as 1, and the value of non as 0. All lesions are superimposed into one image and integrated. The relationship between the lesions in this superimposed image and anxiety after cerebral infarction was analyzed. Results: The lesions were basically concentrated around the lateral ventricle, and they were mainly concentrated around the lateral ventricle. Conclusion: Patients with acute cerebral infarction in the lateral ventricle or basal ganglia are more prone to post-stroke anxiety. This has a certain evaluation value for the prognosis of future cerebral infarction, and has a certain understanding of the exploration of complications, and has a certain understanding of the exploration of complications. 展开更多
关键词 Acute Cerebral Infarction Anxiety MRI Brain Functional Network
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Extracting Multiple Nodes in a Brain Region of Interest for Brain Functional Network Estimation and Classification 认领 引用
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作者 Chengcheng Wang Haimei Wang +1 位作者 Yifan Qiao Yining Zhang 《Journal of Applied Mathematics and Physics》 2022年第11期3408-3423,共16页
Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representativ... Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representative signals from brain regions of interest (ROIs) is important. In the past decades, the common method is generally to take a ROI as a node, averaging all the voxel time series inside it to extract a representative signal. However, one node does not represent the entire information of this ROI, and averaging method often leads to signal cancellation and information loss. Inspired by this, we propose a novel model extraction method based on an assumption that a ROI can be represented by multiple nodes. Methods: In this paper, we first extract multiple nodes (the number is user-defined) from the ROI based on two traditional methods, including principal component analysis (PCA), and K-means (Clustering according to the spatial position of voxels). Then, canonical correlation analysis (CCA) was issued to construct BFNs by maximizing the correlation between the representative signals corresponding to the nodes in any two ROIs. Finally, to further verify the effectiveness of the proposed method, the estimated BFNs are applied to identify subjects with autism spectrum disorder (ASD) and mild cognitive impairment (MCI) from health controls (HCs). Results: Experimental results on two benchmark databases demonstrate that the proposed method outperforms the baseline method in the sense of classification performance. Conclusions: We propose a novel method for obtaining nodes of ROId based on the hypothesis that a ROI can be represented by multiple nodes, that is, to extract the node signals of ROIs with K-means or PCA. Then, CCA is used to construct BFNs. 展开更多
关键词 Brain Functional Network Node Selection Pearson’s Correlation Canonical Correlation Analysis Brain Disorder Classification
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The Effect of Bilateral Transcranial Direct Current Stimulation(tDCS)on the Brain Functional Network in Patients with Upper Limb Spasm after Stroke 认领 引用
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作者 WANG Yuan 《Chinese Journal of Biomedical Engineering(English Edition)》 CAS 2024年第4期178-184,共7页
Objective:To observe the effect of bilateral transcranial direct current stimulation(tDCS)on the brain functional network in patients with upper limb spasm after stroke.Methods:An electroencephalograph(EEG)was used to... Objective:To observe the effect of bilateral transcranial direct current stimulation(tDCS)on the brain functional network in patients with upper limb spasm after stroke.Methods:An electroencephalograph(EEG)was used to analyze the effect of the brain functional network before and after the intervention of bilateral tDCS in patients with secondary upper limb spasm after stroke.A total of 44 patients with upper limb spasm after stroke were randomly divided into a real stimulation group and a sham stimulation group.Bilateral tDCS interventions were carried out respectively.The parameters of the brain functional network measured by EEG and the cross-correlation coefficients between various leads before the experiment and at 5 days after the experiment were compared.Results:During the imagination of grasping a mineral water bottle,the degree of nodes in the real stimulation group after stimulation was higher than that before stimulation and that of the sham stimulation group at the same period(P=0.004);during the imagination of releasing the mineral water bottle,the clustering coefficient and global efficiency of nodes in the real stimulation group were both higher than those of the sham stimulation group at the same period(P=0.020,0.032).Before stimulation,the importance scores of the degree of nodes among various leads ranged from 0.562 to 2.081.Among them,the importance scores of Pz,Oz,FP1,FP2,F3,F4,P4,O1,O2,F8,T5,C3,and Fz were all>1.After stimulation,the importance scores of all leads were>1,ranging from 1.911 to 5.580.The real stimulation group had a more obvious effect on P3,O1,O2,Cz,Oz,T4,T6,Fz,FP1,and T5.Conclusion:Bilateral tDCS intervention can enhance the degree of nodes during the imagination of grasping in patients with upper limb spasm after stroke,and enhance the clustering coefficient and global efficiency of nodes during the imagination of releasing.The brain functional network mainly has a more obvious impact on the brain functional areas of the leads in the parietal lobe,occipital lobe,central midline,occipital midline,middle temporal,posterior temporal,parietal midline,and frontal pole regions. 展开更多
关键词 transcranial direct current stimulation(tDCS) stroke upper limb spasm brain functional network
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