Objective:There is increasing evidence that amyotrophic lateral sclerosis(ALS)is a progressive neurodegenerative disease impacting large-scale brain networks.However,it is still unclear which structural networks are a...Objective:There is increasing evidence that amyotrophic lateral sclerosis(ALS)is a progressive neurodegenerative disease impacting large-scale brain networks.However,it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression.This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate.Methods:Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire difusion-weighted magnetic resonance images and construct white matter(WM)networks using tractography methods.The global and regional network properties were compared between ALS and healthy subjects.The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms.Results:Compared with the healthy participants,the patients with ALS showed signifcantly decreased clustering coefcient C_(p)(P=0.0034,t=2.98),normalized clustering coefcientγ(P=0.039,t=2.08),and small‐worldnessσ(P=0.038,t=2.10)at the global network level.The patients also showed decreased regional centralities in motor and non-motor systems including the frontal,temporal and subcortical regions.Using the single-subject structural connection matrix,our classifcation model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%.Conclusion:Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems,extending the classical pathophysiological understanding of ALS as a motor disorder.The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.展开更多
Background:Abnormalities of cortical thickness(CTh)in patients with their first episode psychosis(FEP)have been frequently reported,but findings are inconsistent.Objective:To define the most consistent CTh changes in ...Background:Abnormalities of cortical thickness(CTh)in patients with their first episode psychosis(FEP)have been frequently reported,but findings are inconsistent.Objective:To define the most consistent CTh changes in patients with FEP by meta-analysis of publishedwholebrain studies.Methods:The meta-analysis used seed-based dmapping(SDM)software to obtain the most prominent regional CTh changes in FEP,and meta-regression analyses to explore the effects of demographics and clinical characteristics.The meta-analysis results were verified in an independent sample of 142 FEP patients and 142 age-and sex-matched healthy controls(HCs),using both a vertex-wise and a region of interest analysis,with multiple comparisons correction.Results:The meta-analysis identified lower CTh in the rightmiddle temporal cortex(MTC)extending to superior temporal cortex(STC),insula,and anterior cingulate cortex(ACC)in FEP compared with HCs.No significant correlations were identified between CTh alterations and demographic or clinical variables.These results were replicated in the independent dataset analysis.Conclusion:This study identifies a robust pattern of cortical abnormalities in FEP and extends understanding of gray matter abnormalities and pathological mechanisms in FEP.展开更多
Background Social intelligence refers to an important psychosocial skill set encompassing an array of abilities,including effective self-expression,understanding of social contexts,and acting wisely in social interact...Background Social intelligence refers to an important psychosocial skill set encompassing an array of abilities,including effective self-expression,understanding of social contexts,and acting wisely in social interactions.While there is ample evidence of its importance in various mental health outcomes,particularly social anxiety,little is known on the brain correlates underlying social intelligence and how it can mitigate social anxiety.Objective This research aims to investigate the functional neural markers of social intelligence and their relations to social anxiety.Methods Data of resting-state functional magnetic resonance imaging and behavioral measures were collected from 231 normal students aged 16 to 20 years(48%male).Whole-brain voxel-wise correlation analysis was conducted to detect the functional brain clusters related to social intelligence.Correlation and mediation analyses explored the potential role of social intelligence in the linkage of resting-state brain activities to social anxiety.Results Social intelligence was correlated with neural activities(assessed as the fractional amplitude of low-frequency fluctuations,fALFF)among two key brain clusters in the social cognition networks:negatively correlated in left superior frontal gyrus(SFG)and positively correlated in right middle temporal gyrus.Further,the left SFG fALFF was positively correlated with social anxiety;brain–personality–symptom analysis revealed that this relationship was mediated by social intelligence.Conclusion These results indicate that resting-state activities in the social cognition networks might influence a person's social anxiety via social intelligence:lower left SFG activity→higher social intelligence→lower social anxiety.These may have implication for developing neurobehavioral interventions to mitigate social anxiety.展开更多
基金This study was supported by the funding of 1.3.5 project for disciplines of excellence,West China Hospital,Sichuan University(ZYJC18038)the National Natural Science Foundation of China(81621003,81820108018,81871000,81761128023)+5 种基金the Program for Changjiang Scholars and Innovative Research Team in University(PCSIRT,IRT16R52)of Chinathe Changjiang Scholar Professorship Award(T2014190)of Chinathe CMB Distinguished Professorship Award(F510000/G16916411)administered by the Institute of International Educationthe China Postdoctoral Science Foundation(2019M653427),Sichuan Science and Technology Program(2020YFS0220)Post-Doctor Research Project,West China Hospital,Sichuan University(2019HXBH029)D.L.was supported by the Newton International Fellowship from the Royal Society。
文摘Objective:There is increasing evidence that amyotrophic lateral sclerosis(ALS)is a progressive neurodegenerative disease impacting large-scale brain networks.However,it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression.This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate.Methods:Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire difusion-weighted magnetic resonance images and construct white matter(WM)networks using tractography methods.The global and regional network properties were compared between ALS and healthy subjects.The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms.Results:Compared with the healthy participants,the patients with ALS showed signifcantly decreased clustering coefcient C_(p)(P=0.0034,t=2.98),normalized clustering coefcientγ(P=0.039,t=2.08),and small‐worldnessσ(P=0.038,t=2.10)at the global network level.The patients also showed decreased regional centralities in motor and non-motor systems including the frontal,temporal and subcortical regions.Using the single-subject structural connection matrix,our classifcation model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%.Conclusion:Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems,extending the classical pathophysiological understanding of ALS as a motor disorder.The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.
基金supported by the National Natural Science Foundation of China(grant nos 81621003,81761128023,81820108018,82027808,and 82001795)NIH/NIMH R01MH112189-01,China Postdoctoral Science Foundation(2020M673245)+3 种基金Post-Doctor Research Project of West China Hospital of Sichuan University(2021HXBH025)US-China joint grant(grant nos NSFC81761128023)Instituto de Salud Carlos III/European Union(ERDF/ESF,‘Investing in your future’:CPII19/00009 and PI19/00394)the project SLT006/17/00357,from PERIS 2016-2020(Departament de Salut),CERCA Programme/Generalitat de Catalunya.
文摘Background:Abnormalities of cortical thickness(CTh)in patients with their first episode psychosis(FEP)have been frequently reported,but findings are inconsistent.Objective:To define the most consistent CTh changes in patients with FEP by meta-analysis of publishedwholebrain studies.Methods:The meta-analysis used seed-based dmapping(SDM)software to obtain the most prominent regional CTh changes in FEP,and meta-regression analyses to explore the effects of demographics and clinical characteristics.The meta-analysis results were verified in an independent sample of 142 FEP patients and 142 age-and sex-matched healthy controls(HCs),using both a vertex-wise and a region of interest analysis,with multiple comparisons correction.Results:The meta-analysis identified lower CTh in the rightmiddle temporal cortex(MTC)extending to superior temporal cortex(STC),insula,and anterior cingulate cortex(ACC)in FEP compared with HCs.No significant correlations were identified between CTh alterations and demographic or clinical variables.These results were replicated in the independent dataset analysis.Conclusion:This study identifies a robust pattern of cortical abnormalities in FEP and extends understanding of gray matter abnormalities and pathological mechanisms in FEP.
基金supported by the Key Research and Development Program of Sichuan Province(Grant Nos.2023YFS0084 and 2023YFS0076).
文摘Background Social intelligence refers to an important psychosocial skill set encompassing an array of abilities,including effective self-expression,understanding of social contexts,and acting wisely in social interactions.While there is ample evidence of its importance in various mental health outcomes,particularly social anxiety,little is known on the brain correlates underlying social intelligence and how it can mitigate social anxiety.Objective This research aims to investigate the functional neural markers of social intelligence and their relations to social anxiety.Methods Data of resting-state functional magnetic resonance imaging and behavioral measures were collected from 231 normal students aged 16 to 20 years(48%male).Whole-brain voxel-wise correlation analysis was conducted to detect the functional brain clusters related to social intelligence.Correlation and mediation analyses explored the potential role of social intelligence in the linkage of resting-state brain activities to social anxiety.Results Social intelligence was correlated with neural activities(assessed as the fractional amplitude of low-frequency fluctuations,fALFF)among two key brain clusters in the social cognition networks:negatively correlated in left superior frontal gyrus(SFG)and positively correlated in right middle temporal gyrus.Further,the left SFG fALFF was positively correlated with social anxiety;brain–personality–symptom analysis revealed that this relationship was mediated by social intelligence.Conclusion These results indicate that resting-state activities in the social cognition networks might influence a person's social anxiety via social intelligence:lower left SFG activity→higher social intelligence→lower social anxiety.These may have implication for developing neurobehavioral interventions to mitigate social anxiety.
