Frederik Schlitt

This study develops a brain-based biomarker, PARES, that predicts both pain increases and relief in individuals using fMRI data.

This study compares neural mechanisms of appetitive and aversive pain-related learning using fMRI, finding shared and distinct brain activity patterns during acquisition and extinction.

This study develops a multi-center machine learning model using gray matter cortical thickness to predict individual pain sensitivity, achieving a moderate predictive performance.

The paper identifies a resting-state brain connectivity pattern predictive of individual pain sensitivity without painful stimulation.