Psychopharmacology
March 24, 2022
Timothy Lawn, Ottavia Dipasquale, Alexandros Vamvakas et al.
48 citations
LSD alters functional connectivity in the brain in ways that depend on its interactions with multiple serotonin and dopamine receptors, not only the 5-HT2A receptor. By analyzing brain scans from 15 participants, researchers found that LSD-induced changes in connectivity linked to different receptors corresponded to different subjective effects: serotonin-related receptors were predominantly associated with perceptual changes, while dopamine-related receptors were more tied to alterations in selfhood and cognition. These patterns were distinct, with similar relationships appearing within each receptor family but not between them. The findings suggest that LSD's full effects involve a broader set of receptors than previously emphasized.
bioRxiv (Cold Spring Harbor Laboratory)
Hanna M. Tolle, Andrea I Luppi, Timothy Lawn et al.
1 citation
preprint
A geometric deep learning model called graphTRIP predicts post-treatment depression severity from pretreatment clinical and brain imaging data. Trained on a clinical trial comparing psilocybin and escitalopram, it achieves strong predictive accuracy (r = 0.75) and generalizes to an independent dataset. The model links better outcomes to reduced functional coupling within serotonin systems and broader serotonergic integration with sensory-motor networks. Causal analysis shows a group-level advantage of psilocybin over escitalopram but identifies individuals with specific stress-related neuromodulatory profiles who may benefit more from escitalopram, advancing precision medicine and biomarker discovery in depression.
bioRxiv : the preprint server for biology
June 12, 2026
Timothy Lawn, Johan Nakuci, Steve Cr Williams et al.
Spatial overlap among brain receptor maps derived from PET imaging can distort analyses that model multiple receptors together. Using test-retest fMRI data, the authors show that as more receptors are included in a multivariate model, the reliability of the resulting functional connectivity networks decreases, and this degradation is driven by collinearity among the receptor maps. A univariate approach, modeling each receptor independently, produces more reliable networks and, in a study comparing LSD to placebo, better captured the known role of the 5HT-2A receptor. Spatial collinearity is a fundamental constraint on multivariate molecular-enriched network estimation, and univariate modeling is recommended as a more robust default.