Richard J. Staba

The paper introduces an unbiased whole-brain analysis method to quantify injury burden after traumatic brain injury, showing it is statistically robust and captures longitudinal changes without preselected ROIs.

This study compares the effects of hand and electric drilling craniectomy on TBI in rats, finding that electric drilling causes more tissue damage and seizures.

PyHFO is a lightweight deep learning platform for efficient HFO detection in EEG data, achieving 50x speed improvements over standard methods.

This study compares two functional connectivity measures in a rat TBI model, finding distinct temporal and spatial changes in connectivity patterns post-injury.

This study identifies subregional cerebellar atrophy patterns in epilepsy syndromes using MRI data from 1,602 patients and 1,022 controls, finding significant volume loss in posterior lobe regions.

This paper develops a deep learning algorithm to detect and classify epileptogenic high-frequency oscillations (eHFOs) from intracranial EEG data with high accuracy.

This study shows that scalp EEG high frequency oscillations (HFOs) and their coupling with slow-wave activity correlate with active infantile spasms and can predict them better than hypsarrhythmia.