Diverse frontoparietal connectivity supports semantic prediction and integration in sentence comprehension.

IF 4.4 2区 医学 Q1 NEUROSCIENCES Journal of Neuroscience Pub Date : 2024-11-12 DOI:10.1523/JNEUROSCI.1404-24.2024
Yaji He, Ximing Shao, Chang Liu, Chen Fan, Elizabeth Jefferies, Meichao Zhang, Xiaoqing Li
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Abstract

Predictive processing in parietal, temporal, frontal, and sensory cortex allows us to anticipate future meanings to maximize the efficiency of language comprehension, with the temporoparietal junction (TPJ) and inferior frontal gyrus (IFG) thought to be situated towards the top of a predictive hierarchy. Although the regions underpinning this fundamental brain function are well-documented, it remains unclear how they interact to achieve efficient comprehension. To this end we recorded functional magnetic resonance imaging (fMRI) in 22 participants (11 males) while they comprehended sentences presented part-by-part, in which we manipulated the constraint provided by sentential contexts on upcoming semantic information. Using this paradigm, we examined the connectivity patterns of bilateral TPJ and IFG during anticipatory phases (i.e., before the onset of targets) and integration phases (i.e., after the onset of targets). When upcoming semantic content was highly predictable in strong-constraint contexts, both left TPJ and bilateral IFG showed stronger visual coupling, while right TPJ showed stronger connectivity with regions within control, default mode, and visual networks, including IFG, parahippocampal gyrus, posterior cingulate, and fusiform gyrus. These connectivity patterns were weaker when predicted semantic content appeared, in line with predictive coding theory. Conversely, for less predictable content, these connectivity patterns were stronger during the integration phase. Overall, these results suggest that both top-down semantic prediction and bottom-up integration during predictive processing are supported by flexible coupling of frontoparietal regions with control, memory, and sensory systems.Significance Statement Recent work has revealed the neural basis of predictive language comprehension. However, it remains unclear how brain regions change their connectivity in a dynamic fashion to support comprehension in highly predictive and less predictive contexts. Here, we show that stronger frontoparietal connectivity with cognitive control, memory, and sensory areas supports top-down prediction generation in strong-constraint contexts; these connectivity patterns are reduced when the anticipated information appears. This pattern is reversed when upcoming sensory input is unpredictable; connectivity is stronger after word inputs have been presented, allowing semantic integration with preceding low-constraint context. Our findings suggest that both top-down semantic prediction and bottom-up semantic integration in language comprehension rely upon diverse functional coupling of higher-order frontoparietal regions with other brain systems.

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不同的额叶连接支持句子理解中的语义预测和整合。
顶叶、颞叶、额叶和感觉皮层的预测处理使我们能够预测未来的含义,从而最大限度地提高语言理解的效率,而颞顶交界处(TPJ)和额下回(IFG)被认为位于预测层次结构的顶端。尽管支持这一基本大脑功能的区域已被详细记录,但它们如何相互作用以实现高效理解仍不清楚。为此,我们记录了 22 名参与者(11 名男性)在理解逐部分呈现的句子时的功能磁共振成像(fMRI),其中我们操纵了句子上下文对即将出现的语义信息的限制。通过这种范式,我们研究了双侧 TPJ 和 IFG 在预期阶段(即目标出现之前)和整合阶段(即目标出现之后)的连接模式。当即将出现的语义内容在强约束情境中具有高度可预测性时,左侧TPJ和双侧IFG都表现出更强的视觉耦合,而右侧TPJ则表现出与控制、默认模式和视觉网络中的区域(包括IFG、海马旁回、扣带回后部和纺锤形回)更强的连接。当出现可预测的语义内容时,这些连接模式就会减弱,这与预测编码理论是一致的。相反,对于可预测性较低的内容,这些连接模式在整合阶段更强。总之,这些结果表明,在预测处理过程中,自上而下的语义预测和自下而上的整合都是由顶叶前部区域与控制、记忆和感觉系统的灵活耦合支持的。然而,目前仍不清楚大脑区域如何以动态的方式改变其连接性,以支持高预测性和低预测性语境下的理解。在这里,我们发现,在强约束语境中,顶叶前部与认知控制、记忆和感觉区域更强的连通性支持自上而下的预测生成;当预期信息出现时,这些连通性模式会降低。当即将到来的感官输入不可预测时,这种模式就会发生逆转;单词输入出现后,连通性就会增强,从而使语义与之前的低约束语境融合在一起。我们的研究结果表明,语言理解中自上而下的语义预测和自下而上的语义整合都依赖于高阶额顶区与其他大脑系统的不同功能耦合。
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来源期刊
Journal of Neuroscience
Journal of Neuroscience 医学-神经科学
CiteScore
9.30
自引率
3.80%
发文量
1164
审稿时长
12 months
期刊介绍: JNeurosci (ISSN 0270-6474) is an official journal of the Society for Neuroscience. It is published weekly by the Society, fifty weeks a year, one volume a year. JNeurosci publishes papers on a broad range of topics of general interest to those working on the nervous system. Authors now have an Open Choice option for their published articles
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