Elizabeth L. Johnson

This study examines slow and fast theta oscillations across the brain in a developmental cohort, revealing spatial gradients and age-related changes linked to attention and memory.

The paper examines high-frequency broadband activity in frontoparietal regions during working memory and decision-making tasks to identify distinct and overlapping neural dynamics.

The paper investigates variability in local field potentials (LFP) and proposes a new metric, CV(log(power ratio)), to quantify power variability, showing its reduction with stimulation and potential inverse relation to power changes.

This study shows that targeting intracranial electrical stimulation to network regions defined by precision functional mapping evokes network-level effects in individual epilepsy patients.

This study investigates how slow and fast theta oscillations in the hippocampal-orbitofrontal network affect working memory performance, finding opposing effects on response times and accuracy.

This study examines neurophysiological differences along the medial temporal lobe axis in pediatric epilepsy patients during memory tasks, finding distinct roles for high-frequency activity and theta oscillations in memory formation.

The paper uses scalp and intracranial EEG to identify network-level signatures of executive control during a working memory task, linking local spectral changes to broader network organization.

This study detects high frequency broadband activity in infants using noninvasive EEG, showing it reliably distinguishes wake from sleep states.

This study maps age-related changes in aperiodic neural activity across brain regions using iEEG data from children and adults, linking these changes to cognitive development and memory performance.

This study investigates theta, beta, and ERP dynamics in mismatch detection during working memory, finding frontal midline theta supports mismatch detection and challenges the role of the N200.

This study examines how high-frequency broadband activity and theta oscillations coordinate during memory processing, showing that specific network connectivity patterns predict memory performance.

The paper decodes working memory-related neural connectivity in mice and humans across tasks, finding task-specific and distributed patterns with high prediction accuracy.