Mats W.J. van Es

The paper introduces a longitudinal neuroimaging dataset of Alzheimer's disease pathology, including multimodal data from patients and controls, to identify and validate novel biomarkers.

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

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.

The paper investigates how distinct alpha brain networks influence perceptual decision-making by analyzing MEG data during a visual detection task, finding that the visual alpha network modulates sensitivity while the sensorimotor alpha network modulates criterion.

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.

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.

This paper introduces a strategy to automate the creation of standardized MATLAB scripts for EEG/MEG analysis using the FieldTrip toolbox, improving reproducibility with minimal user effort.

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 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 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 whether sustained attention modulates visual stimulus representation in a rhythmic way by analyzing MEG signals and decoding visual orientation.

The paper examines how pre-stimulus cortical synchrony relates to behavior during a Simon task, finding no significant differences in synchrony that explain behavioral outcomes.

The paper shows that higher gamma-band power before a visual stimulus predicts stronger evoked responses and faster reaction times.