Background Subjective cognitive decline(SCD)is a preclinical stage along the Alzheimer’s disease(AD)continuum.However,little is known about the aberrant patterns of connectivity and topological alterations of the bra...Background Subjective cognitive decline(SCD)is a preclinical stage along the Alzheimer’s disease(AD)continuum.However,little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD.Methods Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls(HC).Pearson correlation analysis was computed to assess the relationships among network metrics,neuropsychological performance and pathological biomarkers.Finally,we used the multiple kernel learning-support vector machine(MKL-SVM)to differentiate the SCD and HC individuals.Results SCD individuals showed higher nodal topological properties(including nodal strength,nodal global efficiency and nodal local efficiency)associated with amyloid-βlevels and memory function than the HC,and these regions were mainly located in the default mode network(DMN).Moreover,increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus(PHG)and other DMN-related regions was found in SCD individuals compared with HC individuals.These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%.Conclusion The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD.The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.展开更多
基金This work was supported partly by grants from the National Natural Science Foundation of China(No.81822013,81671665)Jiangsu Provincial Key Medical Talents(No.ZDRCA2016085)+2 种基金the Key Research and Development Program of Jiangsu Province of China(BE2016610)the National Key Research and Development Program of China(2016YFC1300500–504)Jiangsu Province Key Medical Discipline(ZDXKA2016020).
文摘Background Subjective cognitive decline(SCD)is a preclinical stage along the Alzheimer’s disease(AD)continuum.However,little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD.Methods Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls(HC).Pearson correlation analysis was computed to assess the relationships among network metrics,neuropsychological performance and pathological biomarkers.Finally,we used the multiple kernel learning-support vector machine(MKL-SVM)to differentiate the SCD and HC individuals.Results SCD individuals showed higher nodal topological properties(including nodal strength,nodal global efficiency and nodal local efficiency)associated with amyloid-βlevels and memory function than the HC,and these regions were mainly located in the default mode network(DMN).Moreover,increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus(PHG)and other DMN-related regions was found in SCD individuals compared with HC individuals.These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%.Conclusion The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD.The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.
