Ethan A. Solomon

This paper uses TMS-EEG to show that prefrontal cortex excitability changes with cognitive load during a multi-source interference task, linking it to theta power and showing region-specific effects.

The paper shows scalp EEG can predict intracranial brain activity in humans, with accuracy varying by region, depth, and frequency.

This study shows that TMS to the DLPFC reduces high-frequency neural activity in the sgACC, suggesting a mechanism for its antidepressant effects.

The paper shows that MTL neurons phase-lock to hippocampal theta oscillations, indicating a role for theta synchronization in memory-related communication across brain regions.

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

This study investigates how TMS affects neural rhythms in different brain regions using intracranial recordings, finding both low-frequency increases and high-frequency decreases in response to stimulation.

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.

The paper shows that neurons in human memory regions phase-lock to hippocampal theta oscillations during memory-related activity.

The paper shows that hippocampal theta oscillations encode semantic and temporal distances between words during memory retrieval.

This paper introduces a multivariate stochastic volatility model to analyze neural data, showing it can decode memory states and reveal brain region desynchronization during memory encoding.

The paper investigates functional connectivity in the human medial temporal lobe during memory tasks, identifying theta-band synchronization and reorganization patterns associated with encoding and retrieval.

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.