Altered patterns of central executive, default mode and salience network activity and connectivity are associated with current and future depression risk in two independent young adult samples

默认模式网络 心理学 轻躁症 焦虑 萧条(经济学) 狂躁 重性抑郁障碍 临床心理学 心理干预 认知 精神科 双相情感障碍 宏观经济学 经济
作者
Michele A. Bertocci,Yvette Afriyie-Agyemang,Renata Rozovsky,Satish Iyengar,Richelle Stiffler,Haris Aslam,Genna Bebko,Mary L. Phillips
出处
期刊:Molecular Psychiatry [Springer Nature]
卷期号:28 (3): 1046-1056 被引量:37
标识
DOI:10.1038/s41380-022-01899-8
摘要

Neural markers of pathophysiological processes underlying the dimension of subsyndromal-syndromal-level depression severity can provide objective, biologically informed targets for novel interventions to help prevent the onset of depressive and other affective disorders in individuals with subsyndromal symptoms, and prevent worsening symptom severity in those with these disorders. Greater functional connectivity (FC) among the central executive network (CEN), supporting emotional regulation (ER) subcomponent processes such as working memory (WM), the default mode network (DMN), supporting self-related information processing, and the salience network (SN), is thought to interfere with cognitive functioning and predispose to depressive disorders. We examined in young adults (1) relationships among activity and FC in these networks and current depression severity, using a paradigm designed to examine WM and ER capacity in n = 90, age = 21.7 (2.0); (2) the extent to which these relationships were specific to depression versus mania/hypomania; (3) whether findings in a first, "discovery" sample could be replicated in a second, independent, "test" sample of young adults n = 96, age = 21.6 (2.1); and (4) whether such relationships also predicted depression at up to 12 months post scan and/or mania/hypomania severity in (n = 61, including participants from both samples, age = 21.6 (2.1)). We also examined the extent to which there were common depression- and anxiety-related findings, given that depression and anxiety are highly comorbid. In the discovery sample, current depression severity was robustly predicted by greater activity and greater positive functional connectivity among the CEN, DMN, and SN during working memory and emotional regulation tasks (all ps < 0.05 qFDR). These findings were specific to depression, replicated in the independent sample, and predicted future depression severity. Similar neural marker-anxiety relationships were shown, with robust DMN-SN FC relationships. These data help provide objective, neural marker targets to better guide and monitor early interventions in young adults at risk for, or those with established, depressive and other affective disorders.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
害怕的萝发布了新的文献求助30
刚刚
刚刚
1秒前
1秒前
叶子发布了新的文献求助10
1秒前
2秒前
研友_LaOyQZ发布了新的文献求助10
2秒前
3秒前
顾矜应助纤指细轻捻采纳,获得10
3秒前
3秒前
CC发布了新的文献求助10
3秒前
3秒前
3秒前
3秒前
4秒前
Seven发布了新的文献求助10
4秒前
4秒前
pyb0919完成签到,获得积分10
5秒前
5秒前
5秒前
hyy发布了新的文献求助10
5秒前
zzzzzz发布了新的文献求助10
5秒前
无花果应助星空采纳,获得10
6秒前
高贵魂幽发布了新的文献求助10
7秒前
Antares发布了新的文献求助10
7秒前
zuozuo发布了新的文献求助10
7秒前
7秒前
郭郭发布了新的文献求助30
8秒前
8秒前
游元稔发布了新的文献求助10
8秒前
Janson发布了新的文献求助10
8秒前
酷炫的书本完成签到,获得积分10
8秒前
研友_VZG7GZ应助lyx采纳,获得10
9秒前
脑洞疼应助yuzhi采纳,获得10
9秒前
研友_LaOyQZ完成签到,获得积分10
9秒前
杜志洪发布了新的文献求助30
9秒前
9秒前
xiaoyou完成签到,获得积分10
10秒前
科研通AI6应助Luo采纳,获得10
10秒前
10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
HIGH DYNAMIC RANGE CMOS IMAGE SENSORS FOR LOW LIGHT APPLICATIONS 1500
Constitutional and Administrative Law 1000
The Social Work Ethics Casebook: Cases and Commentary (revised 2nd ed.). Frederic G. Reamer 800
Holistic Discourse Analysis 600
Vertébrés continentaux du Crétacé supérieur de Provence (Sud-Est de la France) 600
Vertebrate Palaeontology, 5th Edition 530
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5352476
求助须知:如何正确求助?哪些是违规求助? 4485321
关于积分的说明 13962707
捐赠科研通 4385239
什么是DOI,文献DOI怎么找? 2409332
邀请新用户注册赠送积分活动 1401777
关于科研通互助平台的介绍 1375357