一体化PET/MR揭示健康男性脑默认网络内部功能连接和有效连接模式

目的:用一体化PET/MR研究健康年轻男性脑默认网络(DMN)的功能连接(FC)和代谢有效连接(MEC)特征。方法:于2019年1月至2019年5月在西安招募15名健康男性受试者(中位年龄29岁)，使用PET/MR采集所有受试者全脑的 18F-脱氧葡萄糖(FDG) PET、静息功能MRI(fMRI)以及磁化...

Functional connectivity and metabolic effective connectivity patterns within default-mode network in healthy male adults revealed by hybrid PET/MR

Objective To study the functional connectivity(FC)and metabolic effective connectivity(MEC)patterns of the default mode network(DMN)in healthy male adults based on a novel hybrid PET/MR system.Methods Fifteen healthy male adults with median age of 29 years were recruited locally in Xi′an from January to May 2019.All subjects went through PET/MR scan to get the whole brain 18F-fluorodeoxyglucose(FDG)PET,resting-state functional MRI(fMRI)and magnetization prepared rapid gradient echo(MPRAGE)T1 weighted imaging data.CONN18b and statistical parametric mapping(SPM)12 softwares were used to analyze data.The voxel-wise FC and FDG metabolic data were extracted within 4 sub-networks of DMN,which included medial prefrontal cortex(MPFC),posterior cingulate cortex(PCC)and bilateral lateral parietal(LP).The FC and MEC between 4 sub-networks were calculated based on merged resting-state fMRI and metabolic data,and analyzed by one-sample t test separately,with Bonferroni correction.Results FC pathways were all significant within 4 sub-networks of DMN(t values:6.00-7.71,all P<0.008,Bonferroni corrected).Meanwhile,there were significant bi-directional MEC between MPFC and PCC(MPFC to PCC:t=10.03;PCC to MPFC:t=3.73,both P<0.004,Bonferroni corrected),as well as between bilateral LP(LP_L to LP_R:t=5.28;LP_R to LP_L:t=4.76,both P<0.004,Bonferroni corrected).There were significant uni-directional MEC from both MPFC and PCC to bilateral LP(t values:3.44-6.93,all P<0.004,Bonferroni corrected).Conclusions Special FC and MEC patterns exist within DMN.The closely interrelated MPFC and PCC play more important roles in DMN,and they may mediate LP jointly.The novel integrated PET/MR system will bring new perspective on the organization of brain networks,which may deepen the comprehensive understanding of DMN.