Natalie Schaworonkow

This study uses pharmaco-EEG to show diazepam alters alpha oscillation shape features in specific brain regions, supporting the neurophysiological relevance of waveform shape in non-invasive human recordings.

The paper examines waveform shape and spike correlations in bat frontal and auditory cortex, finding distinct dynamics across regions even for similar frequency oscillations.

The paper analyzes resting-state alpha and mu rhythms in children to assess developmental changes and their potential as biomarkers for ADHD and autism, finding no significant differences between groups.

The paper reanalyzes EEG data to show that both oscillations and aperiodic activity are co-modulated during memory encoding.

The paper demonstrates how spatial mixing in EEG/MEG data affects analysis in sensor space, using simulations and resting-state data to show challenges in disentangling overlapping rhythms.

This paper introduces data-driven spatial filters to enhance neuronal oscillation measurement in intracranial electrophysiological recordings, showing improved signal-to-noise ratio and better analysis of oscillation properties.

This study examines longitudinal changes in periodic and aperiodic neuronal activity in infants using EEG data, showing early flattening of the EEG power spectrum and the need to separate these signals for accurate oscillation measurement.

This study shows how spatial synchronization in EEG/MEG can mask non-sinusoidal features of neuronal oscillations, affecting spectral harmonic ratios and amplitude coupling.

The paper compares two EEG filtering methods for extracting the μ-rhythm and assesses their impact on corticospinal excitability measured via TMS.

The paper uses EEG-triggered TMS to show that motor evoked potentials are more modulated by micro-rhythm phase at weaker stimulation intensities, enabling better differentiation of brain states.

This paper investigates how ongoing brain rhythms affect TMS-induced I-wave properties in a computational model, finding that phase and power of rhythms significantly influence responses.

This paper presents a multi-scale computational model of TMS effects on motor cortex, predicting neuron activation patterns and thresholds based on individual head models.