Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patien...Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).展开更多
Network information mining is the study of the network topology,which may answer a large number of applicationbased questions towards the structural evolution and the function of a real system.The question can be rela...Network information mining is the study of the network topology,which may answer a large number of applicationbased questions towards the structural evolution and the function of a real system.The question can be related to how the real system evolves or how individuals interact with each other in social networks.Although the evolution of the real system may seem to be found regularly,capturing patterns on the whole process of evolution is not trivial.Link prediction is one of the most important technologies in network information mining,which can help us understand the evolution mechanism of real-life network.Link prediction aims to uncover missing links or quantify the likelihood of the emergence of nonexistent links from known network structures.Currently,widely existing methods of link prediction almost focus on short-path networks that usually have a myriad of close triangular structures.However,these algorithms on highly sparse or longpath networks have poor performance.Here,we propose a new index that is associated with the principles of structural equivalence and shortest path length(SESPL)to estimate the likelihood of link existence in long-path networks.Through a test of 548 real networks,we find that SESPL is more effective and efficient than other similarity-based predictors in long-path networks.Meanwhile,we also exploit the performance of SESPL predictor and of embedding-based approaches via machine learning techniques.The results show that the performance of SESPL can achieve a gain of 44.09%over GraphWave and 7.93%over Node2vec.Finally,according to the matrix of maximal information coefficient(MIC)between all the similarity-based predictors,SESPL is a new independent feature in the space of traditional similarity features.展开更多
智能反射表面(Intelligent Reflecting Surface,IRS)能够对入射其上的信号进行一定的相位和幅度的变换,从而达到信号的精确传输,提高信号的覆盖和传输效率。但是这种优势都是在已知精确的信道状态信息(Channel State Information,CSI)...智能反射表面(Intelligent Reflecting Surface,IRS)能够对入射其上的信号进行一定的相位和幅度的变换,从而达到信号的精确传输,提高信号的覆盖和传输效率。但是这种优势都是在已知精确的信道状态信息(Channel State Information,CSI)的前提下才能达到。基于IRS元件的无源性,精确的CSI很难得到。针对此问题使用压缩感知(Compressive Sensing,CS)算法结合深度学习(Deep Learning,DL)的方法来解决。使用共链路结构来优化传统的压缩感知算法,能够在更低的导频开销和信噪比(Signal to Noise Ratio,SNR)下获得更好的归一化均方误差(Normalized Mean Square Error,NMSE)。将信道估计问题看作为去噪问题,把优化后的CS算法所得结果看作含有噪声的CSI,使用多重深层降噪块网络对其进一步去噪,得到更加精确的CSI。实验表明,所提算法较对比算法在相同SNR下有更好的精度。展开更多
基金Fundamental Research Funds for the Central Universities in China,No.N161608001 and No.N171903002
文摘Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).
基金supported by the National Natural Science Foundation of China(Grant Nos.61773091 and 62173065)the Industry-University-Research Innovation Fund for Chinese Universities(Grant No.2021ALA03016)+2 种基金the Fund for University Innovation Research Group of Chongqing(Grant No.CXQT21005)the National Social Science Foundation of China(Grant No.20CTQ029)the Fundamental Research Funds for the Central Universities(Grant No.SWU119062).
文摘Network information mining is the study of the network topology,which may answer a large number of applicationbased questions towards the structural evolution and the function of a real system.The question can be related to how the real system evolves or how individuals interact with each other in social networks.Although the evolution of the real system may seem to be found regularly,capturing patterns on the whole process of evolution is not trivial.Link prediction is one of the most important technologies in network information mining,which can help us understand the evolution mechanism of real-life network.Link prediction aims to uncover missing links or quantify the likelihood of the emergence of nonexistent links from known network structures.Currently,widely existing methods of link prediction almost focus on short-path networks that usually have a myriad of close triangular structures.However,these algorithms on highly sparse or longpath networks have poor performance.Here,we propose a new index that is associated with the principles of structural equivalence and shortest path length(SESPL)to estimate the likelihood of link existence in long-path networks.Through a test of 548 real networks,we find that SESPL is more effective and efficient than other similarity-based predictors in long-path networks.Meanwhile,we also exploit the performance of SESPL predictor and of embedding-based approaches via machine learning techniques.The results show that the performance of SESPL can achieve a gain of 44.09%over GraphWave and 7.93%over Node2vec.Finally,according to the matrix of maximal information coefficient(MIC)between all the similarity-based predictors,SESPL is a new independent feature in the space of traditional similarity features.
文摘智能反射表面(Intelligent Reflecting Surface,IRS)能够对入射其上的信号进行一定的相位和幅度的变换,从而达到信号的精确传输,提高信号的覆盖和传输效率。但是这种优势都是在已知精确的信道状态信息(Channel State Information,CSI)的前提下才能达到。基于IRS元件的无源性,精确的CSI很难得到。针对此问题使用压缩感知(Compressive Sensing,CS)算法结合深度学习(Deep Learning,DL)的方法来解决。使用共链路结构来优化传统的压缩感知算法,能够在更低的导频开销和信噪比(Signal to Noise Ratio,SNR)下获得更好的归一化均方误差(Normalized Mean Square Error,NMSE)。将信道估计问题看作为去噪问题,把优化后的CS算法所得结果看作含有噪声的CSI,使用多重深层降噪块网络对其进一步去噪,得到更加精确的CSI。实验表明,所提算法较对比算法在相同SNR下有更好的精度。
