Chetan Gohil

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.

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 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 introduces FUNCOIN, a whole-brain normative model for functional connectivity, and shows it detects abnormal patterns in bipolar disorder and Parkinson's disease better than localized models.

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.

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.

This study investigates neural dynamics during attention shifts between external sensory input and internal working memory using magnetoencephalography.

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.

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