Timothy Lawn

The paper investigates how spatial collinearity among PET receptor maps affects multivariate molecular-enriched network estimation and finds that univariate modelling is more reliable.

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

The paper uses REACT and normative modelling to analyze functional neurocircuits in neuropsychiatric patients, revealing transdiagnostic deviations in neurotransmitter systems and symptom overlaps.

This study uses REACT to examine how propofol affects neurotransmission-related functional connectivity during rest and naturalistic stimulation, revealing changes in GABAergic, noradrenergic, and cholinergic networks.