ArticleNeurobiology of language (Cambridge, Mass.)2024
Cortico-Cerebellar Monitoring of Speech Sequence Production.
Article in Neurobiology of language (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A machine learning-derived speech index as a biomarker for Huntington's disease severity.Journal of neurology · 2026Article
- Dataset for Evaluating the Production of Phonotactically Legal and Illegal Pseudowords.Scientific data · 2025Article
- Frequency-specific cortico-subcortical interaction in continuous speaking and listening.eLife · 2024Article
- A Comparison of Denoising Approaches for Spoken Word Production Related Artefacts in Continuous Multiband fMRI Data.Neurobiology of language (Cambridge, Mass.) · 2024Article
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Authors and funding
8 authors.
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Abstract
In a functional magnetic resonance imaging study, we examined speech error monitoring in a cortico-cerebellar network for two contrasts: (a) correct trials with high versus low articulatory error probability and (b) overtly committed errors versus correct trials. Engagement of the cognitive cerebellar region Crus I in both contrasts suggests that this region is involved in overarching performance monitoring. The activation of cerebellar motor regions (superior medial cerebellum, lobules VI and VIII) indicates the additional presence of a sensorimotor driven implementation of control. The combined pattern of pre-supplementary motor area (active across contrasts) and anterior cingulate cortex (only active in the contrast involving overt errors) activations suggests sensorimotor driven feedback monitoring in the medial frontal cortex, making use of proprioception and auditory feedback through overt errors. Differential temporal and parietal cortex activation across contrasts indicates involvement beyond sensorimotor driven feedback in line with speech production models that link these regions to auditory target processing and internal modeling-like mechanisms. These results highlight the presence of multiple, possibly hierarchically interdependent, mechanisms that support the optimizing of speech production.
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