Motor imagery is the mental representation of an action without overt movement or muscle activation. However, the effects of motor imagery on stroke-induced hand dysfunction and brain neural networks are still unknown...Motor imagery is the mental representation of an action without overt movement or muscle activation. However, the effects of motor imagery on stroke-induced hand dysfunction and brain neural networks are still unknown. We conducted a randomized controlled trial in the China Rehabilitation Research Center. Twenty stroke patients, including 13 males and 7 females, 32–51 years old, were recruited and randomly assigned to the traditional rehabilitation treatment group（PP group, n = 10） or the motor imagery training combined with traditional rehabilitation treatment group（MP group, n = 10）. All patients received rehabilitation training once a day, 45 minutes per session, five times per week, for 4 consecutive weeks. In the MP group, motor imagery training was performed for 45 minutes after traditional rehabilitation training, daily. Action Research Arm Test and the Fugl-Meyer Assessment of the upper extremity were used to evaluate hand functions before and after treatment. Transcranial magnetic stimulation was used to analyze motor evoked potentials in the affected extremity. Diffusion tensor imaging was used to assess changes in brain neural networks. Compared with the PP group, the MP group showed better recovery of hand function, higher amplitude of the motor evoked potential in the abductor pollicis brevis, greater fractional anisotropy of the right dorsal pathway, and an increase in the fractional anisotropy of the bilateral dorsal pathway. Our findings indicate that 4 weeks of motor imagery training combined with traditional rehabilitation treatment improves hand function in stroke patients by enhancing the dorsal pathway. This trial has been registered with the Chinese Clinical Trial Registry（registration number： Chi CTR-OCH-12002238）.展开更多
The piezoelectric materials enable the mutual conversion between mechanical and electrical energy,which drive a multi-billion dollar industry through their applications as sensors,actuators,and energy harvesters.The t...The piezoelectric materials enable the mutual conversion between mechanical and electrical energy,which drive a multi-billion dollar industry through their applications as sensors,actuators,and energy harvesters.The third-rank piezoelectric tensor is the core matrices for piezoelectric materials and their devices.However,the high costs of obtaining full piezoelectric tensor data through either experimental or computational methods make a significant challenge.Here,we propose an equivariant attention tensor graph neural network(EATGNN)that can identify crystal symmetry and remain independent of the reference frame,ultimately enabling the accurate prediction of the complete third-rank piezoelectric tensor.Especially,we perform an irreducible decomposition of the piezoelectric tensor into four irreducible representations to efficiently reserve the symmetry under group transformation operations.Our results further demonstrate that this model performs well in both bulk and twodimensional materials.Finally,combining EATGNN with first-principles calculations,we discovered several potential high-performance piezoelectric materials.展开更多
The present study aims to estimate the in vivo anisotropic conductivities of the White Matter (WM) tissues by means of Magnetic Resonance Electrical Impedance Tomography (MREIT) technique. The realistic anisotropic vo...The present study aims to estimate the in vivo anisotropic conductivities of the White Matter (WM) tissues by means of Magnetic Resonance Electrical Impedance Tomography (MREIT) technique. The realistic anisotropic volume conductor model with different conductivity properties (scalp, skull, CSF, gray matter and WM) is constructed based on the Diffusion Tensor Magnetic Resonance Imaging (DT- MRI) from a healthy human subject. The Radius Basic Function (RBF)-MREIT algorithm of using only one magnetic flux density component was applied to evaluate the eigenvalues of the anisotropic WM with target values set according to the DT-MRI data based on the Wolter’s model, which is more physiologically reliable. The numerical simulations study performed on the five-layer realistic human head model showed that the conductivity reconstruction method had higher accuracy and better robustness against noise. The pilot research was used to judge the feasibility, meaningfulness and reliability of the MREIT applied on the electrical impedance tomography of the complicated human head tissues including anisotropic characteristics.展开更多
Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM,...Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. The above two feature extraction operations are based on the LSTM networks and use their outputs. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. Experiments on the SemEval-2010 Task 8dataset show that our model outperforms most state-of-the-art methods.展开更多
In this paper,we introduce a type of tensor neural network.For the first time,we propose its numerical integration scheme and prove the computational complexity to be the polynomial scale of the dimension.Based on the...In this paper,we introduce a type of tensor neural network.For the first time,we propose its numerical integration scheme and prove the computational complexity to be the polynomial scale of the dimension.Based on the tensor product structure,we develop an efficient numerical integration method by using fixed quadrature points for the functions of the tensor neural network.The corresponding machine learning method is also introduced for solving high-dimensional problems.Some numerical examples are also provided to validate the theoretical results and the numerical algorithm.展开更多
慢性下腰痛(chronic low back pain,CLBP)作为全球致残率最高的肌肉骨骼疾病之一,其临床表现除持续性疼痛和运动功能障碍外,常伴随注意力缺陷、执行功能下降和工作记忆障碍等认知损害。多模态磁共振成像研究为阐明CLBP的神经机制提供了...慢性下腰痛(chronic low back pain,CLBP)作为全球致残率最高的肌肉骨骼疾病之一,其临床表现除持续性疼痛和运动功能障碍外,常伴随注意力缺陷、执行功能下降和工作记忆障碍等认知损害。多模态磁共振成像研究为阐明CLBP的神经机制提供了重要证据:功能磁共振成像(functional magnetic resonance imaging,fMRI)显示前额叶、扣带回和岛叶等认知控制脑区激活异常;静息态fMRI分析揭示默认模式网络、前额叶-顶叶控制网络及显著性网络功能连接失衡;弥散张量成像发现额叶-顶叶通路和胼胝体白质纤维完整性下降,且与认知测评表现相关;磁共振波谱提示N-乙酰天冬氨酸降低及谷氨酸/γ-氨基丁酸平衡失调,反映神经元功能和兴奋-抑制调节受损。现有证据支持CLBP通过“疼痛-情绪-认知”环路和三大脑网络失衡机制导致认知障碍。现有研究主要存在以下局限:大多数为横断面设计,无法确立因果关系;缺乏长期随访数据;样本代表性有限。基于这些局限,未来研究应当:(1)开展纵向追踪和干预性研究以验证神经机制与认知损害的因果关系;(2)整合多模态MRI技术与精细认知行为评估,建立CLBP认知损害的预测模型;(3)探索影像学生物标志物的临床转化价值,为早期识别和干预提供依据。通过解决这些问题,有望为改善CLBP患者的认知结局提供新思路。本综述系统梳理了CLBP相关认知损害的神经影像学研究进展,旨在为从事慢性疼痛与认知神经机制研究的科研人员及临床医生提供理论参考与研究思路,推动该领域从现象描述向机制探索与临床干预的转化。展开更多
基金supported by the National Natural Science Foundation of China,No.U1613228a grant from the Sub-Project under National “Twelfth Five-Year” Plan for Science & Technology Support Project in China,No.2011BAI08B11+1 种基金a grant from the Beijing Municipal Science & Technology Commission in China,No.Z161100002616018the Special Fund for Basic Scientific Research Business of Central Public Scientific Research Institutes in China,No.2014CZ-5,2015CZ-30
文摘Motor imagery is the mental representation of an action without overt movement or muscle activation. However, the effects of motor imagery on stroke-induced hand dysfunction and brain neural networks are still unknown. We conducted a randomized controlled trial in the China Rehabilitation Research Center. Twenty stroke patients, including 13 males and 7 females, 32–51 years old, were recruited and randomly assigned to the traditional rehabilitation treatment group（PP group, n = 10） or the motor imagery training combined with traditional rehabilitation treatment group（MP group, n = 10）. All patients received rehabilitation training once a day, 45 minutes per session, five times per week, for 4 consecutive weeks. In the MP group, motor imagery training was performed for 45 minutes after traditional rehabilitation training, daily. Action Research Arm Test and the Fugl-Meyer Assessment of the upper extremity were used to evaluate hand functions before and after treatment. Transcranial magnetic stimulation was used to analyze motor evoked potentials in the affected extremity. Diffusion tensor imaging was used to assess changes in brain neural networks. Compared with the PP group, the MP group showed better recovery of hand function, higher amplitude of the motor evoked potential in the abductor pollicis brevis, greater fractional anisotropy of the right dorsal pathway, and an increase in the fractional anisotropy of the bilateral dorsal pathway. Our findings indicate that 4 weeks of motor imagery training combined with traditional rehabilitation treatment improves hand function in stroke patients by enhancing the dorsal pathway. This trial has been registered with the Chinese Clinical Trial Registry（registration number： Chi CTR-OCH-12002238）.
基金supported by the National Key R&D Program of China(2019YFE0112000)the Zhejiang Provincial Natural Science Foundation of China(LR21A040001,LDT23F04014F01)the National Natural Science Foundation of China(No.11974307).
文摘The piezoelectric materials enable the mutual conversion between mechanical and electrical energy,which drive a multi-billion dollar industry through their applications as sensors,actuators,and energy harvesters.The third-rank piezoelectric tensor is the core matrices for piezoelectric materials and their devices.However,the high costs of obtaining full piezoelectric tensor data through either experimental or computational methods make a significant challenge.Here,we propose an equivariant attention tensor graph neural network(EATGNN)that can identify crystal symmetry and remain independent of the reference frame,ultimately enabling the accurate prediction of the complete third-rank piezoelectric tensor.Especially,we perform an irreducible decomposition of the piezoelectric tensor into four irreducible representations to efficiently reserve the symmetry under group transformation operations.Our results further demonstrate that this model performs well in both bulk and twodimensional materials.Finally,combining EATGNN with first-principles calculations,we discovered several potential high-performance piezoelectric materials.
文摘The present study aims to estimate the in vivo anisotropic conductivities of the White Matter (WM) tissues by means of Magnetic Resonance Electrical Impedance Tomography (MREIT) technique. The realistic anisotropic volume conductor model with different conductivity properties (scalp, skull, CSF, gray matter and WM) is constructed based on the Diffusion Tensor Magnetic Resonance Imaging (DT- MRI) from a healthy human subject. The Radius Basic Function (RBF)-MREIT algorithm of using only one magnetic flux density component was applied to evaluate the eigenvalues of the anisotropic WM with target values set according to the DT-MRI data based on the Wolter’s model, which is more physiologically reliable. The numerical simulations study performed on the five-layer realistic human head model showed that the conductivity reconstruction method had higher accuracy and better robustness against noise. The pilot research was used to judge the feasibility, meaningfulness and reliability of the MREIT applied on the electrical impedance tomography of the complicated human head tissues including anisotropic characteristics.
基金supported by the National Natural Science Foundation of China (No. 61572505)ChanXueYan Prospective Project of Jiangsu Province (No. BY201502305)
文摘Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. The above two feature extraction operations are based on the LSTM networks and use their outputs. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. Experiments on the SemEval-2010 Task 8dataset show that our model outperforms most state-of-the-art methods.
基金supported in part by the National Key Research and Development Program of China(Grant No.2019YFA0709601)by the National Center for Mathematics and Interdisciplinary Science,CAS.
文摘In this paper,we introduce a type of tensor neural network.For the first time,we propose its numerical integration scheme and prove the computational complexity to be the polynomial scale of the dimension.Based on the tensor product structure,we develop an efficient numerical integration method by using fixed quadrature points for the functions of the tensor neural network.The corresponding machine learning method is also introduced for solving high-dimensional problems.Some numerical examples are also provided to validate the theoretical results and the numerical algorithm.
文摘慢性下腰痛(chronic low back pain,CLBP)作为全球致残率最高的肌肉骨骼疾病之一,其临床表现除持续性疼痛和运动功能障碍外,常伴随注意力缺陷、执行功能下降和工作记忆障碍等认知损害。多模态磁共振成像研究为阐明CLBP的神经机制提供了重要证据:功能磁共振成像(functional magnetic resonance imaging,fMRI)显示前额叶、扣带回和岛叶等认知控制脑区激活异常;静息态fMRI分析揭示默认模式网络、前额叶-顶叶控制网络及显著性网络功能连接失衡;弥散张量成像发现额叶-顶叶通路和胼胝体白质纤维完整性下降,且与认知测评表现相关;磁共振波谱提示N-乙酰天冬氨酸降低及谷氨酸/γ-氨基丁酸平衡失调,反映神经元功能和兴奋-抑制调节受损。现有证据支持CLBP通过“疼痛-情绪-认知”环路和三大脑网络失衡机制导致认知障碍。现有研究主要存在以下局限:大多数为横断面设计,无法确立因果关系;缺乏长期随访数据;样本代表性有限。基于这些局限,未来研究应当:(1)开展纵向追踪和干预性研究以验证神经机制与认知损害的因果关系;(2)整合多模态MRI技术与精细认知行为评估,建立CLBP认知损害的预测模型;(3)探索影像学生物标志物的临床转化价值,为早期识别和干预提供依据。通过解决这些问题,有望为改善CLBP患者的认知结局提供新思路。本综述系统梳理了CLBP相关认知损害的神经影像学研究进展,旨在为从事慢性疼痛与认知神经机制研究的科研人员及临床医生提供理论参考与研究思路,推动该领域从现象描述向机制探索与临床干预的转化。
