摘要
首次采用多模态数据结合机器学习的方法考察了78名学龄儿童(女性39名,平均年龄10.18岁)应激的神经关联。结果表明,儿童应激水平与内侧眶额叶、脑岛、颞上回和辅助运动区的灰质体积呈显著正相关;而与脑岛和顶下小叶之间的功能连接强度呈显著负相关。这表明涉及情绪加工的前额叶−边缘−颞叶脑区可能在儿童应激的个体差异中起着关键作用,而负责整合内外部信息(如,积极的自我评价和外部消极刺激)的脑岛与顶下小叶之间功能同步性的增加与儿童应激的降低有密切关联。基于结构网络的预测分析显示,感觉运动、额顶、突显、视觉和小脑网络对儿童应激水平具有较好的预测能力。研究不仅丰富了儿童应激神经基础的实证证据,而且对儿童应激的早期预防策略和干预手段具有启示意义。
Early life stress(ELS)has been used to describe a broad spectrum of adverse and stressful events,including childhood trauma occurring during neonatal life,early and late childhood,and adolescence.Childhood is a vulnerable time point for stressful events due to an immature brain,which increases the risk of psychopathology in later life.However,to date,studies have focused almost exclusively on adolescents and adults,and little is known about the relationship between ELS and the structural and functional brain changes in children.Here,we adopted a multimodal approach combining voxel-based morphometry(VBM)and functional connectivity(FC)to examine the neural substrates of ELS in children aged 9~12 years.A total of 139 children were recruited for this study.For each participant,the ELS level was assessed and an 8-minute rs-fMRI scan was performed using a 3T Trio scanner.Participants with unqualified data were excluded,resulting in a final sample of 78 participants(39 females;mean age=10.18).For statistical analysis,we used the gray matter volume(GMV)and FC to explore the brain structural and functional correlates of children’s ELS and then used a machine learning method to investigate whether and how structural connectivity profiles in predefined brain networks can predict ELS levels.Additionally,exploratory analyses were performed to investigate potential sex differences and age characteristics in GMV and FC associated with children’s ELS.VBM analysis showed that greater ELS was associated with a larger GMV in the left medial orbitofrontal cortex,right insular cortex,left superior temporal gyrus,and left supplementary motor area.Subsequently,we used these clusters as seed regions to analyze the correlation between FC and stress in children.We found that greater ELS was associated with lower insular-inferior parietal lobule(IPL)connectivity.The results were not influenced by sex,age,total intracranial volume,or head motion.Furthermore,the predictive analysis of machine learning reported that the sensorimotor,frontoparietal,salience,visual,and cerebellar networks could marginally predict ELS scores.Finally,exploratory analyses showed that there were no significant sex differences in the GMV or FC associated with ELS and that significant correlations of ELS with the GMV of the inferior occipital gyrus were mainly manifested in 9-year-old children.Using VBM and FC analyses,we detected structural and functional brain alterations associated with ELS in children aged 9~12 years.Specifically,the VBM analysis mainly reflected that children with high ELS may have abnormal emotional and cognitive functions,such as hypersensitivity to emotional stimuli and over-monitoring of their own behavior.In addition,FC analysis indicated that aberrant interaction of internal and external information may contribute to high ELS in childhood.This study not only provides unique insights into the neural substrates of ELS but may also help identify children who are susceptible to ELS within the general population,which may be advantageous for early prevention strategies and interventions for children.
作者
李为
边子茗
陈曦梅
王俊杰
罗一君
刘永
宋诗情
高笑
陈红
LI Wei;BIAN Ziming;CHEN Ximei;WANG Junjie;LUO Yijun;LIU Yong;SONG Shiqing;GAO Xiao;CHEN Hong(Faculty of Psychology,Southwest University,Ministry of Education,Chongqing 400715,China;Key Laboratory of Cognition and Personality(SWU),Ministry of Education,Chongqing 400715,China)
出处
《心理学报》
CSCD
北大核心
2023年第4期572-587,I0001,共17页
Acta Psychologica Sinica
基金
国家自然科学基金项目(31771237,32271087)
中央高校基本科研业务费专项资金创新团队项目(SWU1709106)资助。
关键词
应激
儿童
灰质体积
静息态功能连接
机器学习
结构网络
early life stress
children
gray matter volume
resting-state functional connectivity
machine learning
structural network