Disrupted effective connectivity of the default, salience and dorsal attention networks in major depressive disorder: a study using spectral dynamic causal modelling of resting-state fMRI

默认模式网络 重性抑郁障碍 静息状态功能磁共振成像 功能磁共振成像 心理学 显著性(神经科学) 神经科学 内科学 精神科 医学 扁桃形结构
作者
Yun Wang,Xiongying Chen,Rui Liu,Zhifang Zhang,Jingjing Zhou,Feng Yuan,Peter Zeidman,Gang Wang,Yuan Zhou
出处
期刊:Journal of Psychiatry & Neuroscience [Joule Inc.]
卷期号:47 (6): E421-E434 被引量:17
标识
DOI:10.1503/jpn.220038
摘要

Background: Understanding the neural basis for major depressive disorder (MDD) is essential for its diagnosis and treatment. Aberrant activation and functional connectivity of the default mode network (DMN), salience network (SN) and dorsal attention network (DAN) have been found consistently in patients with MDD. However, whether effective connectivity within and between these networks is altered in MDD remains unknown. The primary objective of this study was to investigate the effective connectivity of the 3 networks in patients with MDD at rest. Methods: We included 63 patients with MDD (35 first-episode and 28 recurrent) and 74 healthy controls, and collected resting-state functional MRI data for all participants. We defined 15 regions of interest from the 3 functional brain networks of interest using group independent component analysis. We estimated the coupling parameters that reflected the causal interactions among these regions using spectral dynamic causal modelling. We used parametric empirical Bayes to determine commonalities across groups, differences between patients with MDD and healthy controls, and differences between patients with recurrent and first-episode MDD. Results: We found positive (excitatory) connections within each network, negative (inhibitory) connections from the SN and DAN to the DMN, and positive connections from the DAN to the SN across groups. Compared to healthy controls, patients with MDD showed increased positive connections within the DMN, a decreased absolute value of negative connectivity from the SN to the DMN, and increased positive connections from the SN to the DAN. We also found that patients with recurrent MDD showed remarkably different effective connections compared to patients with first-episode MDD, especially related to the DAN. Limitations: Because of the relatively small sample size and the unclear medication history of the MDD sample, the present findings are in need of replication. Conclusion: These findings suggest that effective connectivity among high-order brain functional networks during rest was disrupted in patients with MDD. Moreover, patients with recurrent MDD exhibited different effective connections compared to patients with first-episode MDD. These differences in effective connectivity might provide new insights into the neural substrates of MDD.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
123发布了新的文献求助10
1秒前
ding应助MM采纳,获得10
1秒前
ll完成签到 ,获得积分10
1秒前
2秒前
CodeCraft应助傲娇书易采纳,获得50
2秒前
3秒前
大模型应助泡面公主采纳,获得10
4秒前
科研通AI2S应助Marciu33采纳,获得10
4秒前
科研通AI6应助科研通管家采纳,获得20
5秒前
幸福店员发布了新的文献求助10
5秒前
在水一方应助科研通管家采纳,获得10
5秒前
5秒前
香蕉觅云应助科研通管家采纳,获得10
5秒前
打打应助科研通管家采纳,获得10
5秒前
Mic应助科研通管家采纳,获得10
6秒前
6秒前
wanci应助科研通管家采纳,获得10
6秒前
无极微光应助科研通管家采纳,获得20
6秒前
上官若男应助科研通管家采纳,获得10
6秒前
Orange应助科研通管家采纳,获得10
6秒前
CipherSage应助科研通管家采纳,获得10
6秒前
6秒前
Hello应助qaa2274278941采纳,获得10
7秒前
汉堡包应助科研通管家采纳,获得10
7秒前
完美世界应助科研通管家采纳,获得10
7秒前
7秒前
科研通AI6应助科研通管家采纳,获得10
7秒前
科研通AI2S应助科研通管家采纳,获得10
7秒前
李健应助科研通管家采纳,获得10
7秒前
7秒前
嘞是举仔应助科研通管家采纳,获得20
7秒前
7秒前
8秒前
Wind应助科研通管家采纳,获得10
8秒前
8秒前
8秒前
Lychee完成签到 ,获得积分10
8秒前
SciGPT应助科研通管家采纳,获得10
8秒前
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 2000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5712710
求助须知:如何正确求助?哪些是违规求助? 5211827
关于积分的说明 15268582
捐赠科研通 4864522
什么是DOI,文献DOI怎么找? 2611551
邀请新用户注册赠送积分活动 1561833
关于科研通互助平台的介绍 1519066