Universidad de Buenos Aires, Facultad de Ingeniería, Instituto de Bioingeniería, Buenos Aires, Argentina.
Centre for Psychedelic Research, Division of Academic Psychiatry, Imperial College London, London, United Kingdom.
J Affect Disord. 2024 May 15;353:60-69. doi: 10.1016/j.jad.2024.02.089. Epub 2024 Feb 27.
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.
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.
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.
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.
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.
裸盖菇素是一种血清素能致幻药物，目前正在评估其作为治疗难治性和重度抑郁症的潜在疗法。不同的治疗反应引起了人们对从基线数据预测治疗结果的兴趣。
实施了一个机器学习管道，以研究基线静息状态功能磁共振成像（fMRI）测量的功能连接，作为治疗难治性抑郁症的单药裸盖菇素治疗的症状严重程度的预测因子（16 名患者给予两个 5mg 胶囊，然后间隔一周给予 25mg）。在 22 名参加过中重度抑郁症的裸盖菇素与依地普仑试验的患者样本中进行了可推广性测试（25mg 的裸盖菇素两次单独剂量，间隔 3 周，加 6 周的每日安慰剂；25mg 的裸盖菇素两次单独剂量，间隔 3 周，加 6 周的每日口服依地普仑）。使用两个样本的组合重复了分析。
视觉、默认模式和执行网络的功能连接预测了早期症状的改善，而突显网络则预测了治疗后 24 周的应答者（准确率≈0.9）。推广性能具有边缘意义。联合样本分析得出了一致的结果。额枕和额颞耦合分别预测了早期和晚期症状的减轻。
参与者人数和两个数据集之间的差异限制了研究结果的推广性，而缺乏安慰剂组限制了其特异性。
基线神经生理测量可以预测裸盖菇素治疗抑郁症的结果。未来基于更大数据集的研究应努力评估这些预测的可推广性。
