Suppr超能文献

语义整合需求调节大脑中的大规模网络交互。

Semantic Integration Demands Modulate Large-Scale Network Interactions in the Brain.

作者信息

Nieberlein Laura, Martin Sandra, Williams Kathleen A, Gussew Alexander, Cyriaks Sophia D, Scheer Maximilian, Rampp Stefan, Prell Julian, Hartwigsen Gesa

机构信息

Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

Wilhelm Wundt Institute for Psychology, Leipzig University, Leipzig, Germany.

出版信息

Hum Brain Mapp. 2024 Dec 15;45(18):e70113. doi: 10.1002/hbm.70113.

Abstract

The ability to integrate semantic information into the context of a sentence is essential for human communication. Several studies have shown that the predictability of a final keyword based on the sentence context influences semantic integration on the behavioral, neurophysiological, and neural level. However, the architecture of the underlying network interactions for semantic integration across the lifespan remains unclear. In this study, 32 healthy participants (30-75 years) performed an auditory cloze probability task during functional magnetic resonance imaging (fMRI), requiring lexical decisions on the sentence's final words. Semantic integration demands were implicitly modulated by presenting sentences with expected, unexpected, anomalous, or pseudoword endings. To elucidate network interactions supporting semantic integration, we combined univariate task-based fMRI analyses with seed-based connectivity and between-network connectivity analyses. Behavioral data revealed typical semantic integration effects, with increased integration demands being associated with longer response latencies and reduced accuracy. Univariate results demonstrated increased left frontal and temporal brain activity for sentences with higher integration demands. Between-network interactions highlighted the role of task-positive and default mode networks for sentence processing with increased semantic integration demands. Furthermore, increasing integration demands led to a higher number of behaviorally relevant network interactions, suggesting that the increased between-network coupling becomes more relevant for successful task performance as integration demands increase. Our findings elucidate the complex network interactions underlying semantic integration across the aging continuum. Stronger interactions between various task-positive and default mode networks correlated with more efficient processing of sentences with increased semantic integration demands. These results may inform future studies with healthy old and clinical populations.

摘要

将语义信息整合到句子语境中的能力对于人类交流至关重要。多项研究表明,基于句子语境对最后一个关键词的可预测性会在行为、神经生理学和神经层面影响语义整合。然而,贯穿一生的语义整合背后的网络交互结构仍不清楚。在本研究中,32名健康参与者(30 - 75岁)在功能磁共振成像(fMRI)期间执行了听觉完形概率任务,要求对句子的最后一个单词进行词汇判断。通过呈现具有预期、意外、异常或伪词结尾的句子,语义整合需求被隐性调节。为了阐明支持语义整合的网络交互,我们将基于任务的单变量fMRI分析与基于种子的连通性分析和网络间连通性分析相结合。行为数据揭示了典型的语义整合效应,整合需求增加与反应潜伏期延长和准确性降低相关。单变量结果表明,对于整合需求较高的句子,左额叶和颞叶脑区活动增加。网络间交互突出了任务积极网络和默认模式网络在处理语义整合需求增加的句子时的作用。此外,整合需求增加导致与行为相关的网络交互数量增多,这表明随着整合需求增加,网络间耦合增强对于成功完成任务变得更加重要。我们的研究结果阐明了整个衰老过程中语义整合背后复杂的网络交互。各种任务积极网络和默认模式网络之间更强的交互与更有效地处理语义整合需求增加的句子相关。这些结果可能为未来针对健康老年人和临床人群的研究提供参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e417/11669845/053ac5d39db2/HBM-45-e70113-g008.jpg

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