Vanderbilt University, Nashville, Tennessee 37203
Vanderbilt University, Nashville, Tennessee 37203.
J Neurosci. 2023 Jan 4;43(1):142-154. doi: 10.1523/JNEUROSCI.0529-21.2022. Epub 2022 Nov 16.
Language comprehension requires the rapid retrieval and integration of contextually appropriate concepts ("semantic cognition"). Current neurobiological models of semantic cognition are limited by the spatial and temporal restrictions of single-modality neuroimaging and lesion approaches. This is a major impediment given the rapid sequence of processing steps that have to be coordinated to accurately comprehend language. Through the use of fused functional magnetic resonance imaging and electroencephalography analysis in humans ( = 26 adults; 15 females), we elucidate a temporally and spatially specific neurobiological model for real-time semantic cognition. We find that semantic cognition in the context of language comprehension is supported by trade-offs between widespread neural networks over the course of milliseconds. Incorporation of spatial and temporal characteristics, as well as behavioral measures, provide convergent evidence for the following progression: a hippocampal/anterior temporal phonological semantic retrieval network (peaking at ∼300 ms after the sentence final word); a frontotemporal thematic semantic network (∼400 ms); a hippocampal memory update network (∼500 ms); an inferior frontal semantic syntactic reappraisal network (∼600 ms); and nodes of the default mode network associated with conceptual coherence (∼750 ms). Additionally, in typical adults, mediatory relationships among these networks are significantly predictive of language comprehension ability. These findings provide a conceptual and methodological framework for the examination of speech and language disorders, with additional implications for the characterization of cognitive processes and clinical populations in other cognitive domains. The present study identifies a real-time neurobiological model of the meaning processes required during language comprehension (i.e., "semantic cognition"). Using a novel application of fused magnetic resonance imaging and electroencephalography in humans, we found that semantic cognition during language comprehension is supported by a rapid progression of widespread neural networks related to meaning, meaning integration, memory, reappraisal, and conceptual cohesion. Relationships among these systems were predictive of individuals' language comprehension efficiency. Our findings are the first to use fused neuroimaging analysis to elucidate language processes. In so doing, this study provides a new conceptual and methodological framework in which to characterize language processes and guide the treatment of speech and language deficits/disorders.
语言理解需要快速检索和整合上下文适当的概念（“语义认知”）。当前的语义认知神经生物学模型受到单模态神经影像学和损伤方法的空间和时间限制。鉴于必须协调快速处理步骤才能准确理解语言，这是一个主要障碍。通过在人类中使用融合功能磁共振成像和脑电图分析（= 26 名成年人；15 名女性），我们阐明了实时语义认知的具有时间和空间特异性的神经生物学模型。我们发现，语言理解背景下的语义认知是通过在毫秒级的过程中广泛的神经网络之间的权衡来支持的。纳入空间和时间特征以及行为测量为以下进展提供了收敛性证据：一个海马体/前颞叶语音语义检索网络（在句子结尾词后约 300 毫秒时达到峰值）；一个额颞主题语义网络（约 400 毫秒）；一个海马体记忆更新网络（约 500 毫秒）；一个下额叶语义句法重新评估网络（约 600 毫秒）；以及与概念连贯性相关的默认模式网络节点（约 750 毫秒）。此外，在典型成年人中，这些网络之间的中介关系对语言理解能力具有显著的预测作用。这些发现为言语和语言障碍的检查提供了一个概念和方法框架，并对其他认知领域的认知过程和临床人群的特征描述具有额外的意义。本研究确定了语言理解过程中所需的实时语义处理神经生物学模型（即“语义认知”）。使用融合磁共振成像和脑电图在人类中的新应用，我们发现，语言理解过程中的语义认知是由与意义、意义整合、记忆、再评估和概念凝聚力相关的广泛神经网络的快速进展支持的。这些系统之间的关系预测了个体的语言理解效率。我们的发现是首次使用融合神经影像学分析来阐明语言过程。通过这样做，本研究提供了一个新的概念和方法框架，用于描述语言过程并指导言语和语言缺陷/障碍的治疗。
