Redefining diagnostic lesional status in temporal lobe epilepsy with artificial intelligence.

Despite decades of advancements in diagnostic MRI, 30-50% of temporal lobe epilepsy (TLE) patients remain categorized as "non-lesional" (i.e., MRI negative or MRI-) based on visual assessment by human experts.

Transformer-based neural speech decoding from surface and depth electrode signals.

This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e.

A corollary discharge circuit in human speech.

When we vocalize, our brain distinguishes self-generated sounds from external ones. A corollary discharge signal supports this function in animals; however, in humans, its exact origin and temporal dynamics remain unknown. We report electrocorticographic recordings in neurosurgical patients and a connectivity analysis framework based on Granger causality that reveals major neural communications.

Decoding words during sentence production: Syntactic role encoding and structure-dependent dynamics revealed by ECoG.

Sentence production is the uniquely human ability to transform complex thoughts into strings of words. Despite the importance of this process, language production research has primarily focused on single words. However, it remains a largely untested assumption that the principles of word production generalize to more naturalistic utterances like sentences.

Adding the third dimension: 3D convolutional neural network diagnosis of temporal lobe epilepsy.

Convolutional neural networks (CNN) show great promise for translating decades of research on structural abnormalities in temporal lobe epilepsy into clinical practice. Three-dimensional CNNs typically outperform two-dimensional CNNs in medical imaging. Here we explore for the first time whether a three-dimensional CNN outperforms a two-dimensional CNN for identifying temporal lobe epilepsy-specific features on MRI.