BACKGROUND Sepsis is a life-threatening condition defined by organ dysfunction,triggered by a dysregulated host response to infection.there is limited published literature combining cognitive impairment with topologic...BACKGROUND Sepsis is a life-threatening condition defined by organ dysfunction,triggered by a dysregulated host response to infection.there is limited published literature combining cognitive impairment with topological property alterations in brain networks in sepsis survivors.Therefore,we employed graph theory and Granger causality analysis(GCA)methods to analyze resting-state functional magnetic resonance imaging(rs-fMRI)data,aiming to explore the topological alterations in the brain networks of intensive care unit(ICU)sepsis survivors.Using correlation analysis,the interplay between topological property alterations and cognitive impairment was also investigated.AIM To explore the topological alterations of the brain networks of sepsis survivors and their correlation with cognitive impairment.METHODS Sixteen sepsis survivors and nineteen healthy controls from the community were recruited.Within one month after discharge,neurocognitive tests were administered to assess cognitive performance.Rs-fMRI was acquired and the topological properties of brain networks were measured based on graph theory approaches.GCA was conducted to quantify effective connectivity(EC)between brain regions showing positive topological alterations and other regions in the brain.The correlations between topological properties and cognitive were analyzed.RESULTS Sepsis survivors exhibited significant cognitive impairment.At the global level,sepsis survivors showed lower normalized clustering coefficient(γ)and small-worldness(σ)than healthy controls.At the local level,degree centrality(DC)and nodal efficiency(NE)decreased in the right orbital part of inferior frontal gyrus(ORBinf.R),NE decreased in the left temporal pole of superior temporal gyrus(TPOsup.L)whereas DC and NE increased in the right cerebellum Crus 2(CRBLCrus2.R).Regarding directional connection alterations,EC from left cerebellum 6(CRBL6.L)to ORBinf.R and EC from TPOsup.L to right cerebellum 1(CRBLCrus1.R)decreased,whereas EC from right lingual gyrus(LING.R)to TPOsup.L increased.The implementation of correlation analysis revealed a negative correlation between DC in CRBLCrus2.R and both Mini-mental state examination(r=-0.572,P=0.041)and Montreal cognitive assessment(MoCA)scores(r=-0.629,P=0.021)at the local level.In the CRBLCrus2.R cohort,a negative correlation was identified between NE and MoCA scores,with a statistically significant result of r=-0.633 and P=0.020.CONCLUSION Frontal,temporal and cerebellar topological property alterations are possibly associated with cognitive impairment of ICU sepsis survivors and may serve as biomarkers for early diagnosis.展开更多
BACKGROUND Cognitive decline in type 2 diabetes mellitus(T2DM)occurs years before the onset of clinical symptoms.Early detection of this incipient cognitive decline stage,which is T2DM without mild cognitive impairmen...BACKGROUND Cognitive decline in type 2 diabetes mellitus(T2DM)occurs years before the onset of clinical symptoms.Early detection of this incipient cognitive decline stage,which is T2DM without mild cognitive impairment,is critical for clinical intervention,yet it remains elusive and challenging to identify.AIM To identify structural changes in the brains of T2DM patients without cognitive impairment to gain insights into the early-stage cognitive decline.METHODS Using diffusion tensor imaging(DTI),we constructed structural brain networks in 47 T2DM patients and 47 age-/sex-matched healthy controls.Machine learning models incorporating connectivity features were developed to classify T2DM brains and predict disease duration.RESULTS T2DM patients exhibited reduced global/local efficiency and small-worldness,alongside weakened connectivity in cortical regions but enhanced subcortical-frontal connections,suggesting compensatory mechanisms.A classification model leveraging 18 connectivity features achieved 92.5%accuracy in distinguishing T2DM brains.Structural connectivity patterns further predicted disease onset with an error of±1.9 years.CONCLUSION Our findings reveal early-stage brain network reorganization in T2DM,highlighting subcortical-frontal connectivity as a compensatory biomarker.The high-accuracy models demonstrate the potential of DTI-based biomarkers for preclinical cognitive decline detection.展开更多
To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited pene...To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited penetrable visibility graph(LPVG)is proposed.Firstly,seven types of radar antenna scans are analyzed,which include the circular scan,sector scan,helical scan,raster scan,conical scan,electromechanical hybrid scan and two-dimensional electronic scan.Then,the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network,and the feature parameters are extracted.Finally,the recognition result is obtained by using a support vector machine(SVM)classifier.The experimental results show that the recognition accuracy and noise resistance of this new method are improved,where the average recognition accuracy for radar antenna type is at least 90%when the signalto-noise ratio(SNR)is 5 dB and above.展开更多
The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions,emotions,evaluations and attitudes that people hold about the entities and their attributes from the text.The word...The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions,emotions,evaluations and attitudes that people hold about the entities and their attributes from the text.The word level affective cognition becomes an important topic in sentiment analysis.Extracting the(attribute,opinion word)binary relationship by word segmentation and dependency parsing,and labeling those by existing emotional dictionary combined with webpage information and manual annotation,this paper constitutes a binary relationship knowledge base.By using knowledge embedding method,embedding each element in(attribute,opinion,opinion word)as a word vector into the Knowledge Graph by TransG,and defining an algorithm to distinguish the opinion between the attribute word vector and the opinion word vector.Compared with traditional method,this engine has the advantages of high processing speed and low occupancy,which makes up the time-costing and high calculating complexity in the former methods.展开更多
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
Using resting-state functional magnetic resonance imaging (fMRI) technology to assist in identifying brain diseases has great potential. In the identification of brain diseases, graph-based models have been widely use...Using resting-state functional magnetic resonance imaging (fMRI) technology to assist in identifying brain diseases has great potential. In the identification of brain diseases, graph-based models have been widely used, where graph represents the similarity between patients or brain regions of interest. In these models, constructing high-quality graphs is of paramount importance. Researchers have proposed various methods for constructing graphs from different perspectives, among which the simplest and most popular one is Pearson Correlation (PC). Although existing methods have achieved significant results, these graphs are usually fixed once they are constructed, and are generally operated separately from downstream task. Such a separation may result in neither the constructed graph nor the extracted features being ideal. To solve this problem, we use the graph-optimized locality preserving projection algorithm to extract features and the population graph simultaneously, aiming in higher identification accuracy through a task-dependent automatic optimization of the graph. At the same time, we incorporate supervised information to enable more flexible modelling. Specifically, the proposed method first uses PC to construct graph as the initial feature for each subject. Then, the projection matrix and graph are iteratively optimized through graph-optimization locality preserving projections based on semi-supervised learning, which fully employs the knowledge in various transformation spaces. Finally, the obtained projection matrix is applied to construct the subject-level graph and perform classification using support vector machines. To verify the effectiveness of the proposed method, we conduct experiments to identify subjects with mild cognitive impairment (MCI) and Autism spectrum disorder (ASD) from normal controls (NCs), and the results showed that the classification performance of our method is better than that of the baseline method.展开更多
基金Supported by National Natural Science Foundation of China,No.82372182,No.82172131,and No.U23A20421Training Project of the Leading Expert Team:"Jiyang Medical Elites",No.RC2023-004.
文摘BACKGROUND Sepsis is a life-threatening condition defined by organ dysfunction,triggered by a dysregulated host response to infection.there is limited published literature combining cognitive impairment with topological property alterations in brain networks in sepsis survivors.Therefore,we employed graph theory and Granger causality analysis(GCA)methods to analyze resting-state functional magnetic resonance imaging(rs-fMRI)data,aiming to explore the topological alterations in the brain networks of intensive care unit(ICU)sepsis survivors.Using correlation analysis,the interplay between topological property alterations and cognitive impairment was also investigated.AIM To explore the topological alterations of the brain networks of sepsis survivors and their correlation with cognitive impairment.METHODS Sixteen sepsis survivors and nineteen healthy controls from the community were recruited.Within one month after discharge,neurocognitive tests were administered to assess cognitive performance.Rs-fMRI was acquired and the topological properties of brain networks were measured based on graph theory approaches.GCA was conducted to quantify effective connectivity(EC)between brain regions showing positive topological alterations and other regions in the brain.The correlations between topological properties and cognitive were analyzed.RESULTS Sepsis survivors exhibited significant cognitive impairment.At the global level,sepsis survivors showed lower normalized clustering coefficient(γ)and small-worldness(σ)than healthy controls.At the local level,degree centrality(DC)and nodal efficiency(NE)decreased in the right orbital part of inferior frontal gyrus(ORBinf.R),NE decreased in the left temporal pole of superior temporal gyrus(TPOsup.L)whereas DC and NE increased in the right cerebellum Crus 2(CRBLCrus2.R).Regarding directional connection alterations,EC from left cerebellum 6(CRBL6.L)to ORBinf.R and EC from TPOsup.L to right cerebellum 1(CRBLCrus1.R)decreased,whereas EC from right lingual gyrus(LING.R)to TPOsup.L increased.The implementation of correlation analysis revealed a negative correlation between DC in CRBLCrus2.R and both Mini-mental state examination(r=-0.572,P=0.041)and Montreal cognitive assessment(MoCA)scores(r=-0.629,P=0.021)at the local level.In the CRBLCrus2.R cohort,a negative correlation was identified between NE and MoCA scores,with a statistically significant result of r=-0.633 and P=0.020.CONCLUSION Frontal,temporal and cerebellar topological property alterations are possibly associated with cognitive impairment of ICU sepsis survivors and may serve as biomarkers for early diagnosis.
基金Supported by National Natural Science Foundation of China,No.82104698,No.82330058,No.T2341014,and No.32200923.
文摘BACKGROUND Cognitive decline in type 2 diabetes mellitus(T2DM)occurs years before the onset of clinical symptoms.Early detection of this incipient cognitive decline stage,which is T2DM without mild cognitive impairment,is critical for clinical intervention,yet it remains elusive and challenging to identify.AIM To identify structural changes in the brains of T2DM patients without cognitive impairment to gain insights into the early-stage cognitive decline.METHODS Using diffusion tensor imaging(DTI),we constructed structural brain networks in 47 T2DM patients and 47 age-/sex-matched healthy controls.Machine learning models incorporating connectivity features were developed to classify T2DM brains and predict disease duration.RESULTS T2DM patients exhibited reduced global/local efficiency and small-worldness,alongside weakened connectivity in cortical regions but enhanced subcortical-frontal connections,suggesting compensatory mechanisms.A classification model leveraging 18 connectivity features achieved 92.5%accuracy in distinguishing T2DM brains.Structural connectivity patterns further predicted disease onset with an error of±1.9 years.CONCLUSION Our findings reveal early-stage brain network reorganization in T2DM,highlighting subcortical-frontal connectivity as a compensatory biomarker.The high-accuracy models demonstrate the potential of DTI-based biomarkers for preclinical cognitive decline detection.
基金supported by the China Postdoctoral Science Foundation(2015M572694,2016T90979).
文摘To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited penetrable visibility graph(LPVG)is proposed.Firstly,seven types of radar antenna scans are analyzed,which include the circular scan,sector scan,helical scan,raster scan,conical scan,electromechanical hybrid scan and two-dimensional electronic scan.Then,the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network,and the feature parameters are extracted.Finally,the recognition result is obtained by using a support vector machine(SVM)classifier.The experimental results show that the recognition accuracy and noise resistance of this new method are improved,where the average recognition accuracy for radar antenna type is at least 90%when the signalto-noise ratio(SNR)is 5 dB and above.
基金This research is supported by the Key Program of National Natural Science Foundation of China(Grant Nos.U1536201 and U1405254)the National Natural Science Foundation of China(Grant No.61472092).
文摘The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions,emotions,evaluations and attitudes that people hold about the entities and their attributes from the text.The word level affective cognition becomes an important topic in sentiment analysis.Extracting the(attribute,opinion word)binary relationship by word segmentation and dependency parsing,and labeling those by existing emotional dictionary combined with webpage information and manual annotation,this paper constitutes a binary relationship knowledge base.By using knowledge embedding method,embedding each element in(attribute,opinion,opinion word)as a word vector into the Knowledge Graph by TransG,and defining an algorithm to distinguish the opinion between the attribute word vector and the opinion word vector.Compared with traditional method,this engine has the advantages of high processing speed and low occupancy,which makes up the time-costing and high calculating complexity in the former methods.
文摘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.
文摘Using resting-state functional magnetic resonance imaging (fMRI) technology to assist in identifying brain diseases has great potential. In the identification of brain diseases, graph-based models have been widely used, where graph represents the similarity between patients or brain regions of interest. In these models, constructing high-quality graphs is of paramount importance. Researchers have proposed various methods for constructing graphs from different perspectives, among which the simplest and most popular one is Pearson Correlation (PC). Although existing methods have achieved significant results, these graphs are usually fixed once they are constructed, and are generally operated separately from downstream task. Such a separation may result in neither the constructed graph nor the extracted features being ideal. To solve this problem, we use the graph-optimized locality preserving projection algorithm to extract features and the population graph simultaneously, aiming in higher identification accuracy through a task-dependent automatic optimization of the graph. At the same time, we incorporate supervised information to enable more flexible modelling. Specifically, the proposed method first uses PC to construct graph as the initial feature for each subject. Then, the projection matrix and graph are iteratively optimized through graph-optimization locality preserving projections based on semi-supervised learning, which fully employs the knowledge in various transformation spaces. Finally, the obtained projection matrix is applied to construct the subject-level graph and perform classification using support vector machines. To verify the effectiveness of the proposed method, we conduct experiments to identify subjects with mild cognitive impairment (MCI) and Autism spectrum disorder (ASD) from normal controls (NCs), and the results showed that the classification performance of our method is better than that of the baseline method.
