Leyao Yu

The paper creates a probabilistic functional atlas of brain regions using extraoperative electrocortical stimulation data, revealing variability in functional organization and highlighting key areas for language and motor functions.

This study shows that higher diffusion-weighting strengths (b-values) improve connectome reconstruction accuracy by reducing fiber orientation uncertainty, but SNR loss limits benefits beyond b=6000 s/mm2.

The paper identifies a left-lateralized dorsolateral prefrontal network involved in semantic processing during word retrieval using ECoG data from patients.

The paper identifies the source and timing of a corollary discharge signal in human speech using ECoG recordings and Granger-causality analysis.

This paper introduces a deep learning framework for decoding speech from neural signals, achieving high correlation with original speech spectrograms and providing open-source code.

This study investigates auditory suppression during speech production in humans and finds a correlation between suppression and sensitivity to auditory feedback, suggesting suppression aids speech monitoring.

This study maps speech error timing and location during cortical stimulation, finding distinct latency patterns that suggest functional roles in speech planning and motor execution.

This study uses a deep learning model to decode speech from neural signals, revealing that frontal and temporal cortex process both feedforward and feedback information during speech production.