Both activation and deactivation of functional networks support increased sentence processing costs
The research on the neural correlates underlying the language system has gradually moved away from the traditional Broca-Wernicke framework to a network perspective in the past 15 years. Language processing is found to be supported by the co-activation of both core and peripheral brain regions. Howe...
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doaj-49b369bd5fbb495e9bbd02a5400551fd2020-12-17T04:47:14ZengElsevierNeuroImage1095-95722021-01-01225117475Both activation and deactivation of functional networks support increased sentence processing costsYanyu Xiong0Sharlene Newman1Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405-7007, United States; Corresponding author.Alabama Life Research Institute, University of Alabama, Tuscaloosa, AL 35487, United StatesThe research on the neural correlates underlying the language system has gradually moved away from the traditional Broca-Wernicke framework to a network perspective in the past 15 years. Language processing is found to be supported by the co-activation of both core and peripheral brain regions. However, the dynamic co-activation patterns of these brain regions serving different language functions remain to be fully revealed. The present functional magnetic resonance imaging (fMRI) study focused on sentence processing at different syntactic complexity levels to examine how the co-activation of different brain networks will be modulated by increased processing costs. Chinese relative clauses were used to probe the two dimensions of syntactic complexity: embeddedness (left-branching vs. center-embedded) and gap-filler dependency (subject-gap vs. object-gap) using the general linear model (GLM) approach, independent component analysis (ICA) and graph theoretical analysis. In contrast to localized activation revealed by the GLM approach, ICA identified more extensive networks both positively and negatively correlated with the task. We found that the posterior default mode network was anti-correlated to the gap-filler integration costs with increased deactivation for the left-branching object relative clauses compared to subject relative clauses, suggesting the involvement of this network in leveraging the cognitive resources based on the complexity level of the language task. Concurrent activation and deactivation of networks were found to be associated with the higher costs induced by center-embedding and its interaction with gap-filler integration. The graph theoretical analysis further unveiled that center-embeddedness imposed more attentional demand on the subject relative clause, as characterized by its higher degree and strength in the ventral attention network, and higher processing costs of syntactic reanalysis on the object relative clause, as characterized by increased intermodular connections of the language network with other networks. The results suggest that network activation and deactivation profiles are modulated by different dimensions of syntactic complexity to serve the higher demand of creating a coherent semantic representation.http://www.sciencedirect.com/science/article/pii/S1053811920309605Chinese relative clauseFunctional networkDeactivationGap-filler integrationCenter-embedding |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yanyu Xiong Sharlene Newman |
spellingShingle |
Yanyu Xiong Sharlene Newman Both activation and deactivation of functional networks support increased sentence processing costs NeuroImage Chinese relative clause Functional network Deactivation Gap-filler integration Center-embedding |
author_facet |
Yanyu Xiong Sharlene Newman |
author_sort |
Yanyu Xiong |
title |
Both activation and deactivation of functional networks support increased sentence processing costs |
title_short |
Both activation and deactivation of functional networks support increased sentence processing costs |
title_full |
Both activation and deactivation of functional networks support increased sentence processing costs |
title_fullStr |
Both activation and deactivation of functional networks support increased sentence processing costs |
title_full_unstemmed |
Both activation and deactivation of functional networks support increased sentence processing costs |
title_sort |
both activation and deactivation of functional networks support increased sentence processing costs |
publisher |
Elsevier |
series |
NeuroImage |
issn |
1095-9572 |
publishDate |
2021-01-01 |
description |
The research on the neural correlates underlying the language system has gradually moved away from the traditional Broca-Wernicke framework to a network perspective in the past 15 years. Language processing is found to be supported by the co-activation of both core and peripheral brain regions. However, the dynamic co-activation patterns of these brain regions serving different language functions remain to be fully revealed. The present functional magnetic resonance imaging (fMRI) study focused on sentence processing at different syntactic complexity levels to examine how the co-activation of different brain networks will be modulated by increased processing costs. Chinese relative clauses were used to probe the two dimensions of syntactic complexity: embeddedness (left-branching vs. center-embedded) and gap-filler dependency (subject-gap vs. object-gap) using the general linear model (GLM) approach, independent component analysis (ICA) and graph theoretical analysis. In contrast to localized activation revealed by the GLM approach, ICA identified more extensive networks both positively and negatively correlated with the task. We found that the posterior default mode network was anti-correlated to the gap-filler integration costs with increased deactivation for the left-branching object relative clauses compared to subject relative clauses, suggesting the involvement of this network in leveraging the cognitive resources based on the complexity level of the language task. Concurrent activation and deactivation of networks were found to be associated with the higher costs induced by center-embedding and its interaction with gap-filler integration. The graph theoretical analysis further unveiled that center-embeddedness imposed more attentional demand on the subject relative clause, as characterized by its higher degree and strength in the ventral attention network, and higher processing costs of syntactic reanalysis on the object relative clause, as characterized by increased intermodular connections of the language network with other networks. The results suggest that network activation and deactivation profiles are modulated by different dimensions of syntactic complexity to serve the higher demand of creating a coherent semantic representation. |
topic |
Chinese relative clause Functional network Deactivation Gap-filler integration Center-embedding |
url |
http://www.sciencedirect.com/science/article/pii/S1053811920309605 |
work_keys_str_mv |
AT yanyuxiong bothactivationanddeactivationoffunctionalnetworkssupportincreasedsentenceprocessingcosts AT sharlenenewman bothactivationanddeactivationoffunctionalnetworkssupportincreasedsentenceprocessingcosts |
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