BibTex format

author = {Copa, D and Erritzoe, D and Giribaldi, B and Nutt, D and Carhart-Harris, R and Tagliazucchi, E},
doi = {10.1016/j.jad.2024.02.089},
journal = {J Affect Disord},
pages = {60--69},
title = {Predicting the outcome of psilocybin treatment for depression from baseline fMRI functional connectivity.},
url = {},
volume = {353},
year = {2024}

RIS format (EndNote, RefMan)

AB - BACKGROUND: Psilocybin is a serotonergic psychedelic drug under assessment as a potential therapy for treatment-resistant and major depression. Heterogeneous treatment responses raise interest in predicting the outcome from baseline data. METHODS: A machine learning pipeline was implemented to investigate baseline resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI) as a predictor of symptom severity in psilocybin monotherapy for treatment-resistant depression (16 patients administered two 5 mg capsules followed by 25 mg, separated by one week). Generalizability was tested in a sample of 22 patients who participated in a psilocybin vs. escitalopram trial for moderate-to-severe major depression (two separate doses of 25 mg of psilocybin 3 weeks apart plus 6 weeks of daily placebo vs. two separate doses of 1 mg of psilocybin 3 weeks apart plus 6 weeks of daily oral escitalopram). The analysis was repeated using both samples combined. RESULTS: Functional connectivity of visual, default mode and executive networks predicted early symptom improvement, while the salience network predicted responders up to 24 weeks after treatment (accuracy≈0.9). Generalization performance was borderline significant. Consistent results were obtained from the combined sample analysis. Fronto-occipital and fronto-temporal coupling predicted early and late symptom reduction, respectively. LIMITATIONS: The number of participants and differences between the two datasets limit the generalizability of the findings, while the lack of a placebo arm limits their specificity. CONCLUSIONS: Baseline neurophysiological measurements can predict the outcome of psilocybin treatment for depression. Future research based on larger datasets should strive to assess the generalizability of these predictions.
AU - Copa,D
AU - Erritzoe,D
AU - Giribaldi,B
AU - Nutt,D
AU - Carhart-Harris,R
AU - Tagliazucchi,E
DO - 10.1016/j.jad.2024.02.089
EP - 69
PY - 2024///
SP - 60
TI - Predicting the outcome of psilocybin treatment for depression from baseline fMRI functional connectivity.
T2 - J Affect Disord
UR -
UR -
VL - 353
ER -