Ben Griffin

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

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 predicts biological age from EEG data using kernel methods, showing improved performance over standard approaches.

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