Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
Neuropsychopharmacology. 2023 Jul;48(8):1175-1183. doi: 10.1038/s41386-023-01574-8. Epub 2023 Apr 25.
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 accuracy (91.11%), highlighting the potential of using whole-brain EC to decode or predict subjective effects of LSD in the future.
迷幻剂已成为各种精神疾病有前途的候选治疗方法,鉴于其临床潜力,需要确定其作用的潜在生物标志物。在这里,我们使用回归动态因果建模 (rDCM) 研究了麦角酸二乙酰胺 (LSD) 的神经机制,这是一种评估静息状态功能磁共振成像 (fMRI) 期间全脑有效连通性 (EC) 的新技术。我们对来自两项随机、安慰剂对照、双盲、交叉试验的数据进行了建模,其中 45 名参与者在两项静息状态 fMRI 会议中接受了 100μg LSD 和安慰剂的治疗。我们使用经典统计学和机器学习方法将 EC 与全脑功能连通性 (FC) 进行了比较。EC 参数的多元分析显示,与安慰剂相比,LSD 下主要表现为更强的区域间连通性和降低的自我抑制,除了枕叶和皮质下区域的区域间连通性减弱和自我抑制增加的明显例外。总的来说,这些发现表明 LSD 扰乱了大脑的兴奋/抑制平衡。值得注意的是,全脑 EC 不仅为 LSD 对大脑兴奋/抑制平衡的影响提供了额外的机制见解,而且 EC 还与 LSD 的整体主观效应相关,并在基于机器学习的分析中以高精度(91.11%)区分实验条件,突出了使用全脑 EC 解码或预测 LSD 主观效应的潜力。