响马读paper

一个要求成员每月至少读一篇文献并打卡的学术交流社群

2022, Human Brain Mapping. DOI: 10.1002/hbm.25985
Prediction of childhood maltreatment and subtypes with personalized functional connectome of large‐scale brain networks
Jiang Zhang , Tianyu Zhao , Jingyue Zhang , Zhiwei Zhang , Hongming Li , Bochao Cheng , Yajing Pang , Huawang Wu , Jiaojian Wang
Abstract:
Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large-scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting-state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject-specific functional networks and functional network connectivities (FNCs). A connectome-based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto-parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity.
2023-04-30 19:59:00
#paper doi:  10.1002/hbm.25985 Prediction of childhood maltreatment and subtypes with personalized functional connectome of large-scale brain networks 童年虐待 (CM) 对儿童的身心健康有着长期的影响。 然而,CM 的神经基础仍不清楚。本研究基于个体化的功能脑网络分区方法,计算功能网络连接 (FNC)和通过儿童创伤问卷 (CTQ) 评估的 CM 影响之间的关联。个体化的 FNC可以很好地预测 CM 总分和子量表分数,涉及到了默认模式网络、额顶叶网络、视觉网络、边缘网络、运动网络、背侧和腹侧注意网络,不同的网络对预测有不同的贡献。
TOP