![]() These differences may impact evaluation of the relevance of each language area in clinical practices such as pre-surgical planning 17. In particular, the common language network of bilinguals in clinically acquired phonemic fluency tasks may differ from that of monolinguals in both the active areas and the connectivity between active nodes. Although the signature of the bilingual language network has been widely investigated, its effect on clinical practice is still unclear. ![]() However, there are known functional differences between the language networks of bilinguals and monolinguals that may affect surgical management 16. In a previous study 15, we established a functional language “core” subnetwork by analyzing 20 healthy subjects without regard to monolingual or multi-lingual status. This task showed effective language lateralization in the frontal lobe of the dominant language hemisphere 14 with optimal language localization 14 and is considered among the first choices in the state-of-the-art fMRI paradigm for clinical applications 6. The silent word generation task, considered a phonemic fluency task, requires phonologic access, verbal working memory, and lexical search activity, which induce strong activation and lateralization of frontal areas 11, 12, 13. We recommend the use of visually administered, silently generated language tasks that activate language areas related to speech comprehension and production through covert speech, relying on semantic and syntactic mental representations that require word retrieval and articulatory planning 7, 8, 9, 10. This study employs the paradigm selection recommendations recently published in a white paper by the American Society of Functional Neuroradiology 6. This understanding may help optimizing and guiding neurosurgical brain tumor resection. The long-term goal of this research is to use fMRI to understand how language is functionally organized in healthy individuals and how it functionally re-organizes in the brain tumor setting. Our model described real and significant findings in multiple scenarios that could not be observed using other methods based on network connectivity alone. We successfully applied this general physics approach to multiple situations, including fMRI analysis of how the brain transition from conscious to subliminal perception 4, and in the investigation of memory consolidation in rodents 5. This research aims for the latter, and is based on our group’s recently published work in graph theory and k-core percolation 3. Our growing understanding of functional language network (FLN) is enabled by improving techniques to acquire data 1, 2 and using increasingly sophisticated techniques to analyze functional data. Human language function is exceptionally complex. K-core analysis showed that Wernicke’s area was engaged by the “core” with weaker connection in L2 than L1. Our results demonstrated a persistent network “core” consisting of Broca’s area, the pre-supplementary motor area, and the premotor area. Starting from active clusters on fMRI, we inferred the persistent functional network across subjects and ran centrality measures to characterize differences. All subjects underwent fMRI with gold-standard clinical language tasks. Sixteen right-handed subjects (mean age 42-years nine males) without neurological history were included: eight native English-speaking monolinguals and eight native Spanish-speaking (L1) bilinguals with acquired English (L2). We investigated the influence of bilingualism on clinical fMRI language tasks and characterized bilingual networks using connectivity metrics to provide a patient care benchmark. Emerging connectivity metrics such as k-core may capture these differences, highlighting crucial network components based on resiliency. Bilingualism requires control of multiple language systems, and may lead to architectural differences in language networks obtained from clinical fMRI tasks.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |