Graham Walter Johnson

Generalized brain-state modeling with KenazLBM

KenazLBM is a model that characterizes and predicts brain states from raw iEEG data, generalizing across subjects with heterogeneous recording channels.

Collapse of Interictal Suppressive Networks Permits Seizure Spread

This paper analyzes peri-ictal network dynamics in drug-resistant epilepsy patients to show that NIZs increase inhibitory signaling to SOZs during seizures, but this suppression is insufficient to prevent seizure onset.

Reward Circuit Local Field Potential Modulations Precede Risk Taking

This study identifies reward circuitry activity predictive of risk-taking in humans using local field potential recordings during a gambling task.

Mesolimbic local field potentials are modulated by motor control

The paper examines how motor control affects mesolimbic local field potentials using electrophysiological recordings during a stop-signal task.

Brain-state modeling using electroencephalography: Application to adaptive closed-loop neuromodulation for epilepsy

This paper introduces BrainState, a self-supervised method to model complex brain states from EEG data, applied to epilepsy patients to capture pre-seizure states and neuromodulation effects.

Depression Attenuates Caudate and Dorsolateral Prefrontal Cortex Alpha and Beta Power Response to Reward

This study investigates how depression affects neural oscillations in the caudate and DLPFC during reward feedback, finding reduced alpha and beta power responses in depressed individuals.

Brain-wide human oscillatory LFP activity during visual working memory

The paper analyzes LFP activity in the human brain during visual working memory tasks to identify task-related oscillatory patterns across different brain regions.

Deep learning segmentation of the nucleus basalis of Meynert on 3T MRI

This paper develops a deep learning model to accurately segment the nucleus basalis of Meynert on 3T MRI, showing improved performance over standard atlases in both healthy subjects and TLE patients.

The Interictal Suppression Hypothesis in Focal Epilepsy: Electrographic and Structural Evaluation

The paper tests the interictal suppression hypothesis in focal epilepsy using electrographic and structural data to identify seizure onset zones and develop a classification model with high accuracy.

Multi-channel deep learning with intracranial neurostimulation can localize seizure onset zones in humans

This paper develops a deep learning model to classify seizure onset zones using cortico-cortical evoked potentials from intracranial neurostimulation recordings.