Suppr超能文献

注意缺陷多动障碍儿童认知需求变化时功能性脑网络组织与动力学的重构

Reconfiguration of Functional Brain Network Organization and Dynamics With Changing Cognitive Demands in Children With Attention-Deficit/Hyperactivity Disorder.

作者信息

Michael Cleanthis, Mitchell Mackenzie E, Cascone Arianna D, Fogleman Nicholas D, Rosch Keri S, Cutts Sarah A, Pekar James J, Sporns Olaf, Mostofsky Stewart H, Cohen Jessica R

机构信息

Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

Neuroscience Curriculum, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

出版信息

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Nov 17. doi: 10.1016/j.bpsc.2024.11.006.

Abstract

BACKGROUND

The pathophysiology of attention-deficit/hyperactivity disorder (ADHD) is characterized by atypical brain network organization and dynamics. Although functional brain networks adaptively reconfigure across cognitive contexts, previous studies have largely focused on network dysfunction during the resting state. In this preliminary study, we examined how functional brain network organization and dynamics flexibly reconfigure across rest and 2 cognitive control tasks with different cognitive demands in 30 children with ADHD and 36 typically developing children (ages 8-12 years).

METHODS

We leveraged graph theoretical analyses to interrogate the segregation (modularity, within-module degree) and integration (global efficiency, node dissociation index) of frontoparietal, cingulo-opercular/salience, default mode, somatomotor, and visual networks. We also conducted edge time series analyses to quantify connectivity dynamics within and between these networks.

RESULTS

Across resting and task-based states, children with ADHD demonstrated significantly lower whole-graph modularity and a greater node dissociation index between default mode and visual networks. Furthermore, a significant task-by-diagnosis interaction was observed for frontoparietal network within-module degree, which decreased from rest to task in children with ADHD but increased in typically developing children. Finally, children with ADHD displayed significantly more dynamic connectivity within and across cingulo-opercular/salience, default mode, and somatomotor networks, especially during task performance. Exploratory analyses revealed associations between network dynamics, cognitive performance, and ADHD symptoms.

CONCLUSIONS

By integrating static and dynamic network analyses across changing cognitive demands, this study provides novel insight into how context-specific, context-general, and timescale-dependent network connectivity is altered in children with ADHD. Our findings highlight the involvement and clinical relevance of both association and sensory/motor systems in ADHD.

摘要

背景

注意力缺陷多动障碍(ADHD)的病理生理学特征为大脑网络组织和动态异常。尽管功能性脑网络会在不同认知情境下适应性地重新配置,但以往研究主要聚焦于静息状态下的网络功能障碍。在这项初步研究中,我们考察了30名ADHD儿童和36名发育正常儿童(8 - 12岁)在静息状态以及两项具有不同认知需求的认知控制任务中,功能性脑网络组织和动态如何灵活地重新配置。

方法

我们利用图论分析来探究额顶叶、扣带回 - 脑岛/突显、默认模式、躯体运动和视觉网络的分离(模块性、模块内度)和整合(全局效率、节点分离指数)。我们还进行了边时间序列分析,以量化这些网络内部和之间的连接动态。

结果

在静息和基于任务的状态下,ADHD儿童表现出显著更低的全图模块性,以及默认模式和视觉网络之间更大的节点分离指数。此外,观察到额顶叶网络模块内度存在显著的任务 - 诊断交互作用,在ADHD儿童中该指标从静息到任务时下降,而在发育正常儿童中则上升。最后,ADHD儿童在扣带回 - 脑岛/突显、默认模式和躯体运动网络内部及之间表现出显著更多的动态连接,尤其是在任务执行期间。探索性分析揭示了网络动态、认知表现和ADHD症状之间的关联。

结论

通过整合跨不同认知需求的静态和动态网络分析,本研究为ADHD儿童中特定情境、一般情境和时间尺度依赖的网络连接如何改变提供了新的见解。我们的发现强调了联合系统和感觉/运动系统在ADHD中的参与及临床相关性。

相似文献

本文引用的文献

1
A precision functional atlas of personalized network topography and probabilities.个性化网络拓扑和概率的精准功能图谱
Nat Neurosci. 2024 May;27(5):1000-1013. doi: 10.1038/s41593-024-01596-5. Epub 2024 Mar 26.
5
Is it time to put rest to rest?是时候让休息休息了吗?
Trends Cogn Sci. 2021 Dec;25(12):1021-1032. doi: 10.1016/j.tics.2021.09.005. Epub 2021 Oct 5.
10
High-amplitude cofluctuations in cortical activity drive functional connectivity.皮质活动中的高强度涨落驱动功能连接。
Proc Natl Acad Sci U S A. 2020 Nov 10;117(45):28393-28401. doi: 10.1073/pnas.2005531117. Epub 2020 Oct 22.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验