ArticleImaging neuroscience (Cambridge, Mass.)
Intracranial mapping of linguistic structure building during listening speech comprehension.
Article in Imaging neuroscience (Cambridge, Mass.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Understanding how the human brain processes linguistic structures during speech comprehension is essential for understanding the neural mechanisms of language processing. Despite prior efforts to map the cortical organization of hierarchical linguistic structures, a comprehensive characterization with both high spatial and temporal resolution remains limited. To address this issue, our study applied intracranial stereo-electroencephalography (sEEG) recordings to map the neural tracking at distinct linguistic timescales in the human brain in 20 participants. We employed a frequency tagging paradigm to tag syllable-, phrase-, and sentence-level structures at specific frequencies. The findings revealed robust neural tracking in the fronto-temporal regions at different linguistic levels, with the primary auditory cortex (Heschl's gyrus) exhibiting the strongest activity at the syllabic level, and the superior temporal and inferior frontal regions engaged in the building of higher-level phrasal and sentential structures. Importantly, the neural tracking responses to hierarchical linguistic structures were spatially differentiated across different brain regions. Furthermore, high-gamma responses detected by intracranial electrodes within the left language network were observed to be sparsely distributed, predominantly concentrated in specific fronto-temporal sites. These results suggest spatially distributed functional specialization in the brain for tracking different levels of linguistic structure, as reflected in distinct neural oscillatory responses. This study advances our understanding of the functional organization in cortical regions during auditory speech comprehension and may offer insights for aligning brain processes in language models.
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