Christine Ahrends

This paper introduces a Hidden Markov Model (HMM) framework to automatically segment beta activity from MEG data, showing good test-retest reliability and multisite transferability.

The paper predicts individual differences in melodic contour processing from resting state functional connectivity, finding higher predictability in sensory association cortices.

This study identifies two distinct beta bands in brain activity using MEG and a Hidden Markov Model, showing different roles in movement initiation, maintenance, and termination.

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.

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

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.

The paper investigates how autistic and typically developing individuals process familiar music using fMRI, finding differences in brain network activity during familiar music listening.