Robert E. Gross

The paper investigates the effects of exercise on motor performance and neurophysiology in a Parkinson's disease mouse model, finding no neuroprotection but identifying exercise-induced excitatory changes in the substantia nigra.

The paper introduces a wireless neurostimulation device that decodes brain states and delivers closed-loop stimulation in sheep, showing reliable classification of movement and modulation of spectral activity.

MuFIX recovers crosstalk-contaminated photometry responses by interpolating across contaminated segments using multi-frequency lock-in amplification, accurately restoring original signals.

The paper identifies post-ictal gamma oscillations (PIGOs) as a potential biomarker for milder hippocampal sclerosis in mesial temporal lobe epilepsy, correlating with better treatment outcomes.

The paper uses in vivo imaging to show that cortical spreading depolarization depletes endoplasmic reticulum calcium, linking ER dynamics to seizure suppression.

This paper develops an active learning framework to personalize brain stimulation therapies, showing it outperforms random sampling in efficiency and accuracy across in silico and in vivo experiments.

This study examines how seizure onset and offset patterns affect cortical and substantia nigra activity in nonhuman primates, showing pattern-dependent changes in neural coherence and activity.

SAFE-OPT is a Bayesian optimization algorithm that incorporates safety constraints to find optimal deep brain stimulation parameters without harmful side-effects, validated in rodent studies.

This paper develops an adaptive wheel exercise system for mice with Parkinson's disease to enable non-aversive, performance-tracked exercise and neurophysiological measurement.

This study evaluates optogenetic stimulation waveforms on hippocampal spectral activity, finding that irregular patterns better mimic natural activity than standard pulses.

This paper introduces an automated algorithm to detect seizure-related events in calcium imaging data, showing accurate detection of pre-ictal spikes and seizure wave propagation in mice.

The paper tests if targeting brain stimulation based on anatomical and functional network properties improves memory in patients.

This study decodes depression symptoms using neural recordings from deep brain stimulation patients, finding oscillatory patterns predictive of depression severity.

The paper identifies cortical networks that reinstated semantic content and temporal context during episodic recall, showing how specialized systems organize memory retrieval.

The paper identifies oscillatory biomarkers of memory variability in TBI patients and controls, showing similar patterns of spectral power and theta connectivity during successful memory formation.

This study examines how direct brain stimulation affects neuronal activity in humans, finding that frequency, amplitude, and electrode location influence whether stimulation inhibits or excites activity.

The paper investigates human hippocampal theta oscillations, finding distinct high (8-Hz) and low (3-Hz) frequencies associated with spatial navigation and non-spatial processes respectively.

This study examines how neurons in the human medial-temporal lobe represent spatial targets during navigation, finding that firing rates change based on target position and heading direction.

The paper analyzes 770 intracranial stimulation sessions to assess the risk of stimulation-induced seizures, finding 3-6% of sessions likely or possibly caused seizures with no adverse outcomes.

The paper uses intracranial EEG data to identify brain networks involved in mental arithmetic, revealing contributions from parietal and extra-parietal regions and hemispheric differences.

The paper identifies trace cells in the human entorhinal cortex that remap their spatial fields to represent memory cues during cued recall.

The paper shows that functional connectivity predicts changes in theta power from electrical stimulation, but only when stimulation occurs in or near white matter.

This study uses network control theory to predictably reconfigure brain network interactions through targeted neurostimulation in humans.

The paper identifies electrophysiological signatures of spatial boundaries in the human subiculum using intracranial recordings during a virtual navigation task.