Alvince Le Arnz Pongos

The paper develops a computational model to test cerebellar functions in speech feedback control, finding that internal modeling, timing dynamics, and multimodal integration explain speech abnormalities in cerebellar degeneration.

The paper investigates the stability of language network responses across different tasks and compares it to the multiple demand network, finding robust language processing in the language network and task-dependent responses in the MD network.

This study shows that sensory prediction errors, measured as reduced speaking-induced suppression in auditory cortex, drive speech sensorimotor adaptation during altered auditory feedback.

This paper uses Bayesian inference to estimate parameters of a state feedback control model for vocal pitch in cerebellar ataxia and healthy controls, finding differences in feedback sensitivity and motor response.

The paper identifies language-responsive neural channels in individual participants using MEG, showing spatial stability within individuals but variability across individuals, leading to greater sensitivity in analyses.

The paper introduces a probabilistic atlas for the language network using fMRI data from 806 individuals to bridge gaps between group-level and individual-level language research.