沉思
萧条(经济学)
心理学
动力学(音乐)
功能连接
临床心理学
神经科学
认知心理学
认知
经济
教育学
宏观经济学
作者
Shu Su,Wenwen Ou,Mohan Ma,Huiguang He,Qianqian Zhang,Mei Huang,Wentao Chen,Aoqian Deng,Kangning Li,Zengzhe Xi,Fanyu Meng,Hui Liang,Sirui Gao,Yilin Peng,Mei Liao,Li Zhang,Mi Wang,Jin Liu,Bangshan Liu,Yumeng Ju
出处
期刊:NeuroImage
[Elsevier BV]
日期:2025-03-27
卷期号:310: 121176-121176
被引量:2
标识
DOI:10.1016/j.neuroimage.2025.121176
摘要
Rumination is a known risk factor for depression relapse. Understanding its neurobiological mechanisms during depression remission can inform strategies to prevent relapse, yet the temporal dynamics of brain networks during rumination in remitted depression remain unclear. Here, we collected rumination induction fMRI data from 42 patients with remitted depression and 41 healthy controls (HCs). Using an energy landscape approach, we investigated the temporal dynamics of brain networks during rumination. The appearance frequency (AF) and transition frequency (TF) metrics were defined to quantify the dynamic properties of brain states. Patients during remission showed higher levels of rumination than HCs. Both groups exhibited four brain states during rumination, which consisted of complementary network group activation (states 1 and 2, states 3 and 4). In patients, the AFs of and reciprocal TFs between states 1 and 2 during rumination were significantly increased, while AFs of states 3 and 4 and reciprocal TFs involving states 1-3, 1-4, 2-3, and 2-4 were decreased, both when compared to HCs and relative to patients themselves during distraction. Moreover, we found that for patients, the AF of state 1 was negatively correlated with rumination levels and marginally positively associated with attention, while the AF of state 2 was negatively associated with performance on attention tasks. Our study revealed altered dynamic characteristics of brain states composed of network groups during rumination in remitted depression. Additionally, the findings suggest that heightened self-focus linked to rumination may impair the brain's ability to efficiently allocate attentional resources.
科研通智能强力驱动
Strongly Powered by AbleSci AI