Andrew J. Quinn

This paper estimates the full-frequency whole-head profile of age effects on resting-state MEG and shows the effect is replicable and robust across datasets and covariates.

The paper examines how asymmetry in subcortical brain structures relates to cortical oscillatory power in resting-state MEG data from healthy adults.

This study examines how age affects resting-state cortical networks using MEG data from 612 healthy adults, finding age-related changes in oscillatory power and coherence, and dynamic network dynamics.

The paper introduces canonical Hidden Markov Models (HMMs) for M/EEG data, enabling consistent network analysis across studies by using pre-trained models instead of training new ones for each dataset.

This study shows rhythmic light stimulation elicits multiple neural responses at different frequencies that separately affect perception, with implications for neuromodulatory techniques.

This study shows higher baseline alpha power is linked to faster visual search performance, challenging the idea that alpha oscillations reduce visual processing.

The paper compares conventional spectral fitting and direct linear modelling of fMRS data during a prolonged motor task, finding increased lactate but inconsistent glutamate changes.

This paper optimizes magnetometer arrays and analysis pipelines for multivariate pattern analysis, finding that 30 sensors are sufficient and advising against signal space separation filters.

The paper uses cross-decoding of MEG data to show semantic activation for pictures occurs around 150 ms and for words around 230 ms.

The paper introduces the GLM-Spectrum framework for analyzing power spectra with confound and covariate modeling, showing its ability to quantify condition differences and age effects in EEG data.

The paper compares HMM and DyNeMo for analyzing task-related oscillatory brain activity, showing DyNeMo detects more task activations than traditional methods.

osl-dynamics is a Python toolbox that models fast brain dynamics in neuroimaging data using machine learning, providing tools for analysis and visualization of oscillatory and network activity.

The paper shows that large-scale cortical networks exhibit cyclical activity patterns across multiple MEG datasets, with implications for cognitive function and behavior.

This study identifies spatiotemporal wave-like patterns of beta bursts in human sensorimotor cortex using magnetoencephalography, showing distinct propagation directions with different anatomical and physiological features.

This paper introduces DyNeMo, a model for dynamic functional brain networks that outperforms HMM in capturing non-exclusive network modes in MEG data.

This paper identifies a frequency doubling artifact in decoding accuracy metrics from neurophysiological data and proposes a filtering approach and alternative decoding method to mitigate it.

The paper investigates beta activity and connectivity during sustained motor tasks and finds that beta activity and connectivity are transient, not sustained, even when motor behavior is sustained.

This study shows that transcranial direct current stimulation (tDCS) changes the waveform shape of gamma oscillations in the visual cortex, specifically affecting the descending half-wave.

This paper defines harmonic relationships in signals using frequency ratios and phase, validates it with simulations and real data, and discusses its implications for analyzing neural oscillations.

The paper introduces itEMD, a method to decompose noisy, non-sinusoidal electrophysiological data into distinct oscillatory modes, showing improved separation and reduced mode mixing compared to existing methods.

The paper develops a framework to analyze oscillatory waveform dynamics by isolating signals and quantifying cycle shape changes using instantaneous frequency, revealing variability in theta oscillations in mouse hippocampal recordings.

This study shows that continuous auditory stimulation modulates visual perception and neural activity without rhythmic structure in the visual stimulus or abrupt auditory onsets.

This study identifies two distinct gamma sub-bands in human motor cortex linked to GABAergic signaling and motor learning through combined MEG and TMS methods.

This study shows reduced cortico-muscular beta coupling in Parkinson's disease patients predicts motor impairment despite comparable grip strength.

This study identifies two distinct neural states during wakeful rest using machine learning and links them to specific psychological features like problem-solving and negative rumination.

This paper introduces a method to estimate spatio-spectral oscillatory brain networks without frequency band restrictions, revealing individual variability in alpha oscillation frequency and spatial structure.

This study investigates how Tau pathology in early Alzheimer's disease affects neurophysiological network dynamics using MEG and PET scans, finding frequency-specific correlations between Tau levels and network efficiency and segregation.

The paper predicts individual task responses from resting-state MEG transient spectral events across multiple tasks and subjects.

This study links neural state transitions during wakeful rest to patterns of ongoing thought and well-being.

The paper identifies frequency-specific phase-coupling networks in resting-state magnetoencephalography data, revealing distinct brain states associated with sensory, motor, and higher-order cognitive networks.