Mark W. Woolrich

This paper introduces M/EEG-FUNCOIN, a normative modeling approach for whole-brain functional connectivity in MEG data, showing improved characterization of age and sex-related patterns and detection of connectivity deviations linked to cognitive ability.

This study investigates the drivers of dynamic functional connectivity in resting-state fMRI using HMMs and finds that mean activity and variance dynamics explain most of the apparent connectivity changes.

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.

This study explores neural fingerprinting using MEG data, showing that brain network dynamics captured by a canonical hidden Markov model can support individual identification across different MEG technologies.

This study validates MEG's ability to measure neural activity across frequency and depth using intracranial EEG data from epileptic patients, showing strong agreement for lower frequencies and superficial sources, and sensitivity to deep structures like the hippocampus.

This paper identifies latent subgroups in functional brain organisation using large-scale resting-state fMRI data from UK Biobank participants, revealing associations with cognitive, lifestyle, and health phenotypes.

The paper uses EEG data from TMS-treated patients to show that changes in anterior default mode brain state dynamics predict depressive symptom improvement during treatment.

The paper compares individual variability in fMRI and MEG neural fingerprints, finding low correspondence at the subject level despite population-level similarity.

This paper introduces bi-cross-validation to evaluate and compare dynamic functional connectivity models in fMRI, showing it balances model fit and complexity effectively.

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 examines how age affects the relationship between brain structure and cognition, finding that models trained on younger subjects predict cognition in older subjects better than vice versa.

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 paper introduces DyNeStE, a model that combines recurrent neural networks with Bayesian inference to capture long-term dynamics in brain networks while maintaining interpretability, outperforming HMMs in simulations and resting-state MEG data.

This paper introduces HIVE, a method using embeddings and HMMs to model individual brain network variability across groups, showing improved performance on simulated and real MEG data.

This paper investigates decoding inner speech using EEG and MEG, finding that silent reading can be decoded with moderate accuracy while inner speech tasks perform at chance level.

The paper shows that replay-DMN coordination develops with age, predicting grid-like representations in the entorhinal cortex during memory formation.

This paper introduces M-DyNeMo, a method that reveals uncoupled dynamics between power and functional connectivity in MEG data, showing task-related modulation of these dynamics.

The paper introduces an HMM-Fisher kernel approach to predict individual traits from brain dynamics using fMRI data, showing it is accurate and reliable compared to other methods.

This paper introduces mPFMs, a new method for multiscale functional brain connectivity analysis that improves prediction of UK Biobank traits compared to standard techniques.

The paper investigates how reward-related neural representations are reactivated during learning to reinforce behavior, finding that reactivation occurs when reward contingencies are consistent across tasks.

This paper uses TDE-HMM to analyze MEG data from a working memory task in MS patients, identifying altered activation patterns in specific brain networks linked to cognitive impairment and treatment effects.

This study compares static and dynamic resting-state networks identified by medium-density EEG and high-density MEG, finding comparable network descriptions with MEG showing higher sensitivity and reproducibility.

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 improves prediction of cognitive traits from fMRI data by stacking multiple dynamic brain network models to reduce variability and enhance accuracy.

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

This paper evaluates individual differences in brain network organization and identifies spatial overlap as indicative of linear additive coupling.

This paper uses TDE-HMM to analyze MEG data and identifies four task-specific brain states associated with working memory processes.

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.

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