Universal neural basis of structure building evidenced by network modulations emerging from Broca's area: The case of Chinese

Abstract

The basic steps in building up language involve binding words of different categories into a hierarchical structure. To what extent these steps are universal or differ across languages is an open issue. Here we examine the neural dynamics of phrase structure building in Chinese—a language that in contrast to other languages heavily depends on contextual semantic information. We used functional magnetic resonance imaging and dynamic causal modeling to identify the relevant brain regions and their dynamic relations. Language stimuli consisted of syntax‐driving determiners, semantics‐embedded classifiers, and nonverbal symbols making up for two‐component sequences manipulated by the factors structure (phrase/list) and number of words (2‐word/1‐word). Processing phrases compared with word lists elicited greater activation in the anterior part of Broca's area, Brodmann area (BA) 45, and the left posterior superior/middle temporal gyri (pSTG/pMTG), while processing two words against one word led to stronger involvement of the left BA 45, BA 44, and insula. Differential network modulations emerging from subparts of Broca's area revealed that phrasal construction in particular highly modulated the direct connection from BA 44 to left pMTG, suggesting BA 44’s primary role in phrase structure building. Conversely, the involvement of BA 45 rather appears sensitive to the reliance on lexico‐semantic information in Chinese. Against the background of previous findings from other languages, the present results indicate that phrase structure building has a universal neural basis within the left fronto‐temporal network. Most importantly, they provide the first evidence demonstrating that the structure‐building network may be modulated by language‐specific characteristics.

Functional Annotation of Human Cognitive States using Deep Graph Convolution

“…This variation on the sensitivity of sample size might be caused by different levels of inter-subject variability in the cognitive demands of the underlying cognitive processes. For instance, large individual variability has been reported in working memory tasks (Fougnie et al, 2012; Osaka et al, 2003), while the language network was consistently activated during the auditory language comprehension across different populations and languages (Friederici, 2011; Wu et al, 2019; Zhang et al, 2017).…”

“…This variation on the sensitivity of sample size might be caused by different levels of inter-subject variability in the cognitive demands of the underlying cognitive processes. For instance, large individual variability has been reported in working memory tasks (Osaka et al , 2003;Fougnie, Suchow and Alvarez, 2012) , while the language network was consistently activated during the auditory language comprehension across different populations and languages (Friederici, 2011;Zhang et al , 2017;Wu, Zaccarella and Friederici, 2019) . The sensitivity of GCN on sample size (A) was investigated by changing the number of independent subjects selected from HCP task-fMRI dataset, ranging from 14 to 1060 with a smaller step before the plateau and a larger step after.…”

The influence of positive emotion and negative emotion on false memory based on EEG signal analysis

“…It is connected with amygdala, orbitofrontal cortex and hippocampus, and participates in the emotional system of the brain [29,30]. BA45, a part of Broca's area, is located in the frontal cortex and is responsible for semantic tasks and word production [31]. Therefore, we can know that in the identification of keywords, positive emotion prompted participants to stimulate more brain resources, emotional brain areas and semantic brain areas were activated at the same time, which increased the participants' semantic association of key-words.…”

Linguistic input drives brain network configuration during language comprehension

“…Combining these two dimensionsi.e., conceptual and formal features - As we pointed out in the introduction, there is an extensive knowledge of the left-lateralized perisylvian circuit underlying the different language-related operations. The functional role played by the different nodes within this circuit in comprehension has been extensively explored (8,11,16). Specifically, there are numerous studies using typical and atypical populations focusing on the functional characterization of the temporal lobei.e., from anterior to posterior and from superior to inferiorand the inferior frontal gyrus (8-10, 16, 17).…”

“…However, despite the significant increase in the knowledge of language processing, it is still not clear whether, how, and where conceptual information mediates the building of syntactic structures (1)(2)(3)(4)(5)(6)(7)(8). The vast majority of studies have explored formal and conceptual factors separately, assuming there is no interaction between them (4,(8)(9)(10)(11). The current study is aimed at shedding light on this issue by pinpointing how the neural network(s) underlying the building of syntactic structures combines the formal and conceptual factors embedded in our linguistic code.…”