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
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
Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits...Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.展开更多
Background:Individuals with subjective memory complaints(SMC)feature a higher risk of cognitive decline and clinical progression of Alzheimer’s disease(AD).However,the pathological mechanism underlying SMC remains un...Background:Individuals with subjective memory complaints(SMC)feature a higher risk of cognitive decline and clinical progression of Alzheimer’s disease(AD).However,the pathological mechanism underlying SMC remains unclear.We aimed to assess the intrinsic connectivity network and its relationship with AD-related pathologies in SMC individuals.Methods:We included 44 SMC individuals and 40 normal controls who underwent both resting-state functional MRI and positron emission tomography(PET).Based on graph theory approaches,we detected local and global functional connectivity across the whole brain by using degree centrality(DC)and eigenvector centrality(EC)respectively.Additionally,we analyzed amyloid deposition and tauopathy via florbetapir-PET imaging and cerebrospinal fluid(CSF)data.The voxel-wise two-sample T-test analysis was used to examine between-group differences in the intrinsic functional network and cerebral amyloid deposition.Then,we correlated these network metrics with pathological results.Results:The SMC individuals showed higher DC in the bilateral hippocampus(HP)and left fusiform gyrus and lower DC in the inferior parietal region than controls.Across all subjects,the DC of the bilateral HP and left fusiform gyrus was positively associated with total tau and phosphorylated tau181.However,no significant between-group difference existed in EC and cerebral amyloid deposition.Conclusion:We found impaired local,but not global,intrinsic connectivity networks in SMC individuals.Given the relationships between DC value and tau level,we hypothesized that functional changes in SMC individuals might relate to pathological biomarkers.展开更多
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.
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.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(LGF19H090023)the National Natural Science Foundation of China(81801785 and 82172056)+5 种基金the National Key Research and Development Program of China(2019YFC1711800)the Key Research and Development Program of Shanxi(2020ZDLSF04-03)This work was partly supported by the grants from the Zhejiang Lab(2019KE0AD01 and 2021KE0AB04)the Zhejiang University Global Partnership Fund(100000-11320)the Shanghai Municipal Science and Technology Major Project(2021SHZDZX0100)the Fundamental Research Funds for the Central Universities.
文摘Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.
基金Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative(ADNI)(National Institutes of Health Grant U01 AG024904)and DOD ADNI(Department of Defense award number W81XWH-12-2-0012)This study was funded by National Key Research and Development Program of China(Grant No.2016YFC1306600)+3 种基金Zhejiang Provincial Natural Science Foundation of China(Grant Nos.LZ14H180001 and Y16H090026)Young ResearchTalents Fund,Chinese Medicine Science,and Technology Project of Zhejiang Province(Grant No.2018ZQ035)the Fundamental Research Funds for the Central Universities(No.2017XZZX001-01)Zhejiang Medicine and Health Science and Technology Program(2018KY418).
文摘Background:Individuals with subjective memory complaints(SMC)feature a higher risk of cognitive decline and clinical progression of Alzheimer’s disease(AD).However,the pathological mechanism underlying SMC remains unclear.We aimed to assess the intrinsic connectivity network and its relationship with AD-related pathologies in SMC individuals.Methods:We included 44 SMC individuals and 40 normal controls who underwent both resting-state functional MRI and positron emission tomography(PET).Based on graph theory approaches,we detected local and global functional connectivity across the whole brain by using degree centrality(DC)and eigenvector centrality(EC)respectively.Additionally,we analyzed amyloid deposition and tauopathy via florbetapir-PET imaging and cerebrospinal fluid(CSF)data.The voxel-wise two-sample T-test analysis was used to examine between-group differences in the intrinsic functional network and cerebral amyloid deposition.Then,we correlated these network metrics with pathological results.Results:The SMC individuals showed higher DC in the bilateral hippocampus(HP)and left fusiform gyrus and lower DC in the inferior parietal region than controls.Across all subjects,the DC of the bilateral HP and left fusiform gyrus was positively associated with total tau and phosphorylated tau181.However,no significant between-group difference existed in EC and cerebral amyloid deposition.Conclusion:We found impaired local,but not global,intrinsic connectivity networks in SMC individuals.Given the relationships between DC value and tau level,we hypothesized that functional changes in SMC individuals might relate to pathological biomarkers.
