Amy Isabella Sentis

This paper uses CRISPR-edited human iPSCs with hypomorphic NOTCH1 to model hypoplastic left heart syndrome, identifying auranofin as a potential therapeutic.

The paper predicts instantaneous heart period from whole brain fMRI data using machine learning, identifying cortical and subcortical brain networks linked to psychological stress as reliable predictors.

The paper compares task-related network profiles and activation patterns in two executive control tasks, finding greater differences in functional correlations than in evoked activity.

The paper improves prediction of cognitive abilities by integrating multiple neuroimaging modalities using machine learning, achieving a 1-4% increase in variance explained compared to single-modality approaches.