ISSN 0439-755X
CN 11-1911/B

心理学报 ›› 2023, Vol. 55 ›› Issue (4): 572-587.doi: 10.3724/SP.J.1041.2023.00572

• 研究报告 • 上一篇    下一篇

9~12岁儿童应激与额颞区的关联: 来自多模态脑影像的证据

李为1, 边子茗1, 陈曦梅1, 王俊杰1, 罗一君1, 刘永1,2, 宋诗情1, 高笑1,2, 陈红1,2()   

  1. 1西南大学心理学部, 重庆 400715
    2西南大学认知与人格教育部重点实验室, 重庆 400715
  • 收稿日期:2022-03-15 发布日期:2022-12-30 出版日期:2023-04-25
  • 通讯作者: 陈红
  • 作者简介:李为和边子茗为共同第一作者。
  • 基金资助:

The relationship between frontotemporal regions and early life stress in children aged 9 to 12: Evidence from multimodal fMRI

LI Wei1, BIAN Ziming1, CHEN Ximei1, WANG Junjie1, LUO Yijun1, LIU Yong1,2, SONG Shiqing1, GAO Xiao1,2, CHEN Hong1,2()   

  1. 1Faculty of Psychology, Southwest University, Chongqing 400715, China
    2Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
  • Received:2022-03-15 Online:2022-12-30 Published:2023-04-25
  • Contact: CHEN Hong


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。

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 (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.

Key words: early life stress, children, gray matter volume, resting-state functional connectivity, machine learning, structural network