Alina Tetereva

The paper evaluates if individualized head models improve source-space EEG functional connectivity predictions of cognition in Parkinson's disease, finding standard templates are sufficient.

This paper introduces a stacking machine learning approach to improve predictability, test-retest reliability, and generalisability of brain-wide associations for cognitive abilities using multimodal MRI data.

This study evaluates Brain Age as a biomarker for fluid cognition in older adults and finds it only marginally improves prediction over chronological age.

This paper shows that integrating task-based fMRI across tasks improves prediction and test-retest reliability of cognitive abilities compared to non-task MRI modalities.

This paper develops a method to detect changes in resting-state fMRI activity after stimulus exposure using Laplacian eigenmaps and wavelet smoothing.

The paper correlates generalized fractional anisotropy in white matter tracts with anxiety scores in healthy participants, finding reproducible associations in corpus callosum and cingulum.

The paper examines changes in BOLD signal variance and scale-free properties during resting-state sessions before and after fear learning and extinction in humans.

This study examines longitudinal changes in resting-state functional connectivity of the amygdala following fear learning and extinction in healthy participants, finding persistent increases in left amygdala connectivity after extinction.

The paper examines the weekly reproducibility of resting-state amygdala functional connectivity in healthy volunteers, finding greater stability in right amygdala connections compared to the left.