Hanna Renvall

This study investigates the relationship between cortical and corticospinal excitability thresholds along the motor cortex using TMS-EMG-EEG in 24 subjects, finding strong correlations and site-specific differences.

The paper identifies five distinct depression phenotypes using MEG-oscillation functional connectivity and low-dimensional brain-symptom associations.

This study uses MEG data to guide TMS timing and location for individualized speech cortical mapping, improving accuracy by aligning TMS with MEG-defined activation patterns.

This paper introduces a Hidden Markov Model (HMM) framework to automatically segment beta activity from MEG data, showing good test-retest reliability and multisite transferability.

The paper uses MEG to show that older adults maintain lexical-semantic proficiency through neural reorganization, preserving sensitivity to semantic congruency despite altered activation patterns.

This study identifies two distinct beta bands in brain activity using MEG and a Hidden Markov Model, showing different roles in movement initiation, maintenance, and termination.

This study uses Bayesian reduced-rank regression to extract stable neural fingerprints from EEG data of children during nREM sleep, showing increased fingerprint stability with age.

This paper introduces an automated method to remove speech artefacts from MEG data using EMG and mutual information, improving the analysis of speech-related neural activity.

This study investigates the heritability of sensorimotor beta rhythm and aperiodic signal in human MEG data, finding high heritability for beta spectral power and aperiodic 1/f signal.

This study assesses the test-retest reliability of sensorimotor beta events and 1/f response using MEG in healthy controls, finding good stability for most features.

The paper investigates how cortical responses to spoken words are encoded with high temporal fidelity, showing that time-locking to speech's spectrotemporal features is crucial for linguistic representation.