The effect of lysergic acid diethylamide (LSD) on whole-brain functional and effective connectivity

作者：Peter Bedford, Daniel J. Hauke, Zheng Wang, Volker Röth, Monika Nagy-Huber, Friederike Holze, Laura Ley, Patrick Vizeli, Matthias E. Liechti, Stefan Borgwardt, Felix Müller, Andreea O. Diaconescu · 发表于：Neuropsychopharmacology · 年份：2023 · DOI：10.1038/s41386-023-01574-8 · 被引用次数：43 · 研究领域：Psychedelics and Drug Studies、Neurotransmitter Receptor Influence on Behavior、Olfactory and Sensory Function Studies

Psychedelics have emerged as promising candidate treatments for various psychiatric conditions, and given their clinical potential, there is a need to identify biomarkers that underlie their effects. Here, we investigate the neural mechanisms of lysergic acid diethylamide (LSD) using regression dynamic causal modelling (rDCM), a novel technique that assesses whole-brain effective connectivity (EC) during resting-state functional magnetic resonance imaging (fMRI). We modelled data from two randomised, placebo-controlled, double-blind, cross-over trials, in which 45 participants were administered 100 μg LSD and placebo in two resting-state fMRI sessions. We compared EC against whole-brain functional connectivity (FC) using classical statistics and machine learning methods. Multivariate analyses of EC parameters revealed predominantly stronger interregional connectivity and reduced self-inhibition under LSD compared to placebo, with the notable exception of weakened interregional connectivity and increased self-inhibition in occipital brain regions as well as subcortical regions. Together, these findings suggests that LSD perturbs the Excitation/Inhibition balance of the brain. Notably, whole-brain EC did not only provide additional mechanistic insight into the effects of LSD on the Excitation/Inhibition balance of the brain, but EC also correlated with global subjective effects of LSD and discriminated experimental conditions in a machine learning-based analysis with high accur...