Hanna M. Tolle

This paper introduces graphTRIP, a geometric deep learning model that predicts antidepressant treatment response using neuroimaging and clinical data, identifying biomarkers and causal mechanisms.

This paper uses brain-fingerprinting to show that psilocybin increases idiosyncratic functional connectivity in the default-mode network, which predicts subjective drug experiences.

This study examines how ayahuasca use affects functional connectomes, finding shared functional spaces and predictive relationships between connectivity patterns and drug experiences.