• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

探索 N 回任务中的大脑-行为关系。

Exploring brain-behavior relationships in the N-back task.

机构信息

Department of Psychological and Brain Sciences, Washington University in Saint Louis, 1 Brookings Drive, Saint Louis, MO, 63130, USA.

Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN, Nijmegen, the Netherlands; Department of Cognitive, Linguistics, and Psychological Sciences, Brown University, 190 Thayer Street, Providence, RI, 02912, USA.

出版信息

Neuroimage. 2020 May 15;212:116683. doi: 10.1016/j.neuroimage.2020.116683. Epub 2020 Feb 27.

DOI:10.1016/j.neuroimage.2020.116683
PMID:32114149
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7781187/
Abstract

Working memory (WM) function has traditionally been investigated in terms of two dimensions: within-individual effects of WM load, and between-individual differences in task performance. In human neuroimaging studies, the N-back task has frequently been used to study both. A reliable finding is that activation in frontoparietal regions exhibits an inverted-U pattern, such that activity tends to decrease at high load levels. Yet it is not known whether such U-shaped patterns are a key individual differences factor that can predict load-related changes in task performance. The current study investigated this question by manipulating load levels across a much wider range than explored previously (N ​= ​1-6), and providing a more comprehensive examination of brain-behavior relationships. In a sample of healthy young adults (n ​= ​57), the analysis focused on a distinct region of left lateral prefrontal cortex (LPFC) identified in prior work to show a unique relationship with task performance and WM function. In this region it was the linear slope of load-related activity, rather than the U-shaped pattern, that was positively associated with individual differences in target accuracy. Comprehensive supplemental analyses revealed the brain-wide selectivity of this pattern. Target accuracy was also independently predicted by the global resting-state connectivity of this LPFC region. These effects were robust, as demonstrated by cross-validation analyses and out-of-sample prediction, and also critically, were primarily driven by the high-load conditions. Together, the results highlight the utility of high-load conditions for investigating individual differences in WM function.

摘要

工作记忆(WM)功能传统上被研究为两个维度:个体内部 WM 负荷的影响,以及个体间任务表现的差异。在人类神经影像学研究中,N-back 任务经常被用于研究这两个方面。一个可靠的发现是,额顶叶区域的激活表现出倒 U 型模式,即活动在高负荷水平下趋于减少。然而,目前尚不清楚这种 U 型模式是否是一个关键的个体差异因素,可以预测与任务表现相关的负荷变化。本研究通过在比以前探索的范围更广的范围内操纵负荷水平(N=1-6),并更全面地研究大脑-行为关系,来探讨这个问题。在一组健康的年轻成年人(n=57)中,分析集中在左外侧前额叶皮层(LPFC)的一个特定区域,该区域在先前的研究中显示出与任务表现和 WM 功能的独特关系。在这个区域,与负荷相关的活动的线性斜率,而不是 U 型模式,与个体差异的目标准确性呈正相关。全面的补充分析揭示了这种模式在大脑范围内的选择性。目标准确性也独立地由该 LPFC 区域的全局静息状态连接性来预测。这些效应是稳健的,如交叉验证分析和样本外预测所示,并且至关重要的是,主要是由高负荷条件驱动的。总之,结果突出了高负荷条件在研究 WM 功能个体差异方面的效用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/e8b6e9dfff37/nihms-1655794-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/178f3de81719/nihms-1655794-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/9a792fab3f7e/nihms-1655794-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/f33863cf9252/nihms-1655794-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/5ec2abff83e3/nihms-1655794-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/e8b6e9dfff37/nihms-1655794-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/178f3de81719/nihms-1655794-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/9a792fab3f7e/nihms-1655794-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/f33863cf9252/nihms-1655794-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/5ec2abff83e3/nihms-1655794-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2931/7781187/e8b6e9dfff37/nihms-1655794-f0005.jpg

相似文献

1
Exploring brain-behavior relationships in the N-back task.探索 N 回任务中的大脑-行为关系。
Neuroimage. 2020 May 15;212:116683. doi: 10.1016/j.neuroimage.2020.116683. Epub 2020 Feb 27.
2
Topologically Reorganized Connectivity Architecture of Default-Mode, Executive-Control, and Salience Networks across Working Memory Task Loads.跨工作记忆任务负荷的默认模式、执行控制和突显网络的拓扑重组连接架构
Cereb Cortex. 2016 Apr;26(4):1501-1511. doi: 10.1093/cercor/bhu316. Epub 2015 Jan 16.
3
Dynamic shifts in brain network activation during supracapacity working memory task performance.超容量工作记忆任务执行过程中脑网络激活的动态变化。
Hum Brain Mapp. 2015 Apr;36(4):1245-64. doi: 10.1002/hbm.22699. Epub 2014 Nov 24.
4
Load-dependent relationships between frontal fNIRS activity and performance: A data-driven PLS approach.基于数据驱动偏最小二乘的方法分析额皮质近红外 fNIRS 活动与表现的负荷相关性
Neuroimage. 2021 Apr 15;230:117795. doi: 10.1016/j.neuroimage.2021.117795. Epub 2021 Jan 24.
5
Dorsolateral Prefrontal Cortex GABA Concentration in Humans Predicts Working Memory Load Processing Capacity.人类背外侧前额叶皮质中的γ-氨基丁酸浓度可预测工作记忆负荷处理能力。
J Neurosci. 2016 Nov 16;36(46):11788-11794. doi: 10.1523/JNEUROSCI.1970-16.2016.
6
The role of neural load effects in predicting individual differences in working memory function.神经负荷效应在预测工作记忆功能个体差异中的作用。
Neuroimage. 2021 Dec 15;245:118656. doi: 10.1016/j.neuroimage.2021.118656. Epub 2021 Oct 19.
7
Load modulation of BOLD response and connectivity predicts working memory performance in younger and older adults.脑血氧水平依赖信号的负载调节与连接预测年轻和老年成年人的工作记忆表现。
J Cogn Neurosci. 2011 Aug;23(8):2030-45. doi: 10.1162/jocn.2010.21560. Epub 2010 Sep 9.
8
Brain connectivity during resting state and subsequent working memory task predicts behavioural performance.静息状态和随后的工作记忆任务期间的大脑连通性可预测行为表现。
Cortex. 2012 Oct;48(9):1187-96. doi: 10.1016/j.cortex.2011.07.006. Epub 2011 Aug 5.
9
Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships.任务诱发的大脑连接促进了大脑-行为关系个体差异的检测。
Neuroimage. 2020 Feb 15;207:116370. doi: 10.1016/j.neuroimage.2019.116370. Epub 2019 Nov 18.
10
Caudate dopamine D1 receptor density is associated with individual differences in frontoparietal connectivity during working memory.尾状核多巴胺 D1 受体密度与工作记忆期间额顶连接的个体差异相关。
J Neurosci. 2011 Oct 5;31(40):14284-90. doi: 10.1523/JNEUROSCI.3114-11.2011.

引用本文的文献

1
Functional near-infrared spectroscopy short-channel regression improves cortical activation estimates of working memory load.功能近红外光谱短通道回归改善了工作记忆负荷的皮质激活估计。
Neurophotonics. 2025 Jul;12(3):035009. doi: 10.1117/1.NPh.12.3.035009. Epub 2025 Aug 21.
2
Selective activation of ipRGC modulates working memory performance.视黑素视网膜神经节细胞的选择性激活可调节工作记忆表现。
PLoS One. 2025 Jun 30;20(6):e0327349. doi: 10.1371/journal.pone.0327349. eCollection 2025.
3
The Neurobiology of Cognitive Fatigue and Its Influence on Effort-Based Choice.

本文引用的文献

1
The Subjective Value of Cognitive Effort is Encoded by a Domain-General Valuation Network.认知努力的主观价值由一个通用的估值网络编码。
J Neurosci. 2019 May 15;39(20):3934-3947. doi: 10.1523/JNEUROSCI.3071-18.2019. Epub 2019 Mar 8.
2
Mapping the human brain's cortical-subcortical functional network organization.绘制人类大脑皮质-皮质下功能网络组织图。
Neuroimage. 2019 Jan 15;185:35-57. doi: 10.1016/j.neuroimage.2018.10.006. Epub 2018 Oct 3.
3
Identification of Two Distinct Working Memory-Related Brain Networks in Healthy Young Adults.
认知疲劳的神经生物学及其对基于努力的选择的影响。
J Neurosci. 2025 Jun 11;45(24):e1612242025. doi: 10.1523/JNEUROSCI.1612-24.2025.
4
Exploring anterior thalamus functional connectivity with cortical regions in prospective memory with ultra-high-field functional MRI.利用超高场功能磁共振成像探索前丘脑与前瞻性记忆中皮质区域的功能连接。
Brain Commun. 2025 Apr 8;7(2):fcaf135. doi: 10.1093/braincomms/fcaf135. eCollection 2025.
5
The Association between Working Hours with Vigilance and Executive Function of Intensive Care Unit Nurses.重症监护病房护士工作时长与警觉性及执行功能之间的关联
J Nurs Manag. 2023 Dec 9;2023:3770404. doi: 10.1155/2023/3770404. eCollection 2023.
6
Effective Motor Skill Learning Induces Inverted-U Load-Dependent Activation in Contralateral Pre-Motor and Supplementary Motor Area.有效的运动技能学习会在对侧运动前区和辅助运动区诱导出倒U型负荷依赖性激活。
Hum Brain Mapp. 2025 Apr 1;46(5):e70208. doi: 10.1002/hbm.70208.
7
Spatiotemporal survival analysis for movement trajectory tracking in virtual reality.虚拟现实中运动轨迹跟踪的时空生存分析
Sci Rep. 2025 Mar 1;15(1):7313. doi: 10.1038/s41598-025-91471-5.
8
Brain compensatory activation during Stroop task in patients with mild cognitive impairment: a functional near-infrared spectroscopy study.轻度认知障碍患者在Stroop任务中的脑代偿性激活:一项功能近红外光谱研究。
Front Aging Neurosci. 2025 Feb 7;17:1470747. doi: 10.3389/fnagi.2025.1470747. eCollection 2025.
9
Tale of Two n-Backs: Diverging Associations of Dorsolateral Prefrontal Cortex Activation With n-Back Task Performance.两种n-回溯任务的故事:背外侧前额叶皮层激活与n-回溯任务表现的不同关联。
J Neurosci Res. 2025 Feb;103(2):e70021. doi: 10.1002/jnr.70021.
10
DomeVR: Immersive virtual reality for primates and rodents.穹顶虚拟现实:适用于灵长类动物和啮齿动物的沉浸式虚拟现实
PLoS One. 2025 Jan 16;20(1):e0308848. doi: 10.1371/journal.pone.0308848. eCollection 2025.
识别健康年轻成年人中两个不同的工作记忆相关脑网络。
eNeuro. 2018 Feb 14;5(1). doi: 10.1523/ENEURO.0222-17.2018. eCollection 2018 Jan-Feb.
4
Combining region- and network-level brain-behavior relationships in a structural equation model.在结构方程模型中结合区域和网络水平的大脑-行为关系。
Neuroimage. 2018 Jan 15;165:158-169. doi: 10.1016/j.neuroimage.2017.10.007. Epub 2017 Oct 10.
5
Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.在心理学中选择预测而不是解释:来自机器学习的教训。
Perspect Psychol Sci. 2017 Nov;12(6):1100-1122. doi: 10.1177/1745691617693393. Epub 2017 Aug 25.
6
Activity flow over resting-state networks shapes cognitive task activations.静息态网络上的活动流塑造了认知任务激活。
Nat Neurosci. 2016 Dec;19(12):1718-1726. doi: 10.1038/nn.4406. Epub 2016 Oct 10.
7
Diffusion Decision Model: Current Issues and History.扩散决策模型:当前问题与历史
Trends Cogn Sci. 2016 Apr;20(4):260-281. doi: 10.1016/j.tics.2016.01.007. Epub 2016 Mar 5.
8
Lateral Prefrontal Cortex Contributes to Fluid Intelligence Through Multinetwork Connectivity.外侧前额叶皮层通过多网络连接对流体智力有贡献。
Brain Connect. 2015 Oct;5(8):497-504. doi: 10.1089/brain.2015.0357. Epub 2015 Sep 23.
9
Dynamic shifts in brain network activation during supracapacity working memory task performance.超容量工作记忆任务执行过程中脑网络激活的动态变化。
Hum Brain Mapp. 2015 Apr;36(4):1245-64. doi: 10.1002/hbm.22699. Epub 2014 Nov 24.
10
The neural bases of distracter-resistant working memory.抗干扰工作记忆的神经基础。
Cogn Affect Behav Neurosci. 2014 Mar;14(1):90-105. doi: 10.3758/s13415-013-0226-y.