Mark W. Woolrich

The paper evaluates how different assumptions in hidden Markov models affect state decomposition in electrophysiological data using synthetic and real data.

The paper compares two models for simulating brain activity across modalities, showing they can replicate functional connectivity and dynamics but with varying performance across modalities.

This paper introduces a multiclass full-epoch decoding approach for M/EEG data that improves accuracy over sliding window methods while enabling interpretation of spatiotemporal 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 paper introduces STRM, a method for analyzing M/EEG data to infer spatiotemporal activity patterns and improve predictive accuracy in neuroscience tasks.

This paper investigates how data characteristics and model parameters affect the ability of time-varying functional connectivity models to detect temporal changes in fMRI data.

This paper introduces a multi-dynamic model to separately analyze time-varying brain activity and functional connectivity in resting fMRI, revealing stronger temporal changes in connectivity and better prediction of behavioral variability.

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.

This study compares microstate analysis and hidden Markov modeling of MEG/EEG data to show they capture different spatial and temporal aspects of brain activity.

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.

This paper introduces sPROFUMO, a hierarchical model for estimating functional brain networks in individuals and populations from large fMRI datasets, showing improved performance over traditional 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 identifies two distinct gamma sub-bands in human motor cortex linked to GABAergic signaling and motor learning through combined MEG and TMS methods.

The paper shows that non-invasive human recordings can reveal state space trajectories of choice processes, finding that choice-irrelevant features are less represented than relevant ones even after extensive training.

The paper develops methods to assess time-varying functional connectivity in fMRI and shows it explains behavioural traits not captured by structural data or time-averaged connectivity.

The paper shows replay bursts occur during activation of the default mode and parietal alpha networks, coordinating information flow between cortical areas.

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 study examines age-related changes in resting-state brain function from childhood to late adulthood using magnetoencephalography, finding increased integration and stability in development and reduced integration in aging.

The paper identifies three dissociable components of oscillatory activity in MEG data that underlie information encoding in human perception.

The paper extends probabilistic functional modes to model subject variability in fMRI data, showing spatial maps capture most cross-subject variability and functional coupling reflects cognitive state.

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 develops a method to estimate transient states in neural networks using EEG and fMRI to show how neurovascular coupling differs across brain networks in older adults.

The paper identifies two modes of neural communication in the human brain—extrinsic coupling via phase synchronization and intrinsic integration via amplitude correlation—during rest and tasks.

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

The paper evaluates and optimizes network modelling methods for fMRI by testing various parcellation, connectivity measures, and classifiers across multiple datasets to find the best predictive pipeline.

This study shows that effective cognitive therapy for PTSD restores balance in brain networks involved in self-referential processing and threat detection.

The paper shows that variability in stimulus-induced gamma responses is linked to spontaneous brain network states, with both trial-to-trial and subject-to-subject differences explained by pre-stimulus network dynamics.