Guet-McCreight Alexandre, Mazza Frank, Prevot Thomas D, Sibille Etienne, Hay Etay
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada.
Department of Physiology, University of Toronto, Toronto, Canada.
PLoS Comput Biol. 2024 Dec 27;20(12):e1012693. doi: 10.1371/journal.pcbi.1012693. eCollection 2024 Dec.
Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of α5-GABAA receptors (α5-PAM) offers a promising effective treatment. However, testing the effect of α5-PAM on human brain activity is limited, meriting the use of detailed simulations. We utilized our previous detailed computational models of human depression microcircuits with reduced SST interneuron inhibition and α5-PAM effects, to simulate EEG of individual microcircuits across depression severity and α5-PAM doses. We developed machine learning models that predicted optimal dose from EEG with high accuracy and recovered microcircuit activity and EEG. This study provides dose prediction models for α5-PAM administration based on EEG biomarkers of depression severity. Given limitations in doing the above in the living human brain, the results and tools we developed will facilitate translation of α5-PAM treatment to clinical use.
重度抑郁症（抑郁症）的治疗往往只有部分疗效，且很大一部分患者对治疗有抵抗性。最近的研究表明，抑郁症患者中生长抑素（SST）中间神经元抑制作用减弱，而通过α5-γ-氨基丁酸A型受体（α5-GABAA）的正向变构调节剂（α5-PAM）增强这种抑制作用的新药理学方法提供了一种有前景的有效治疗方案。然而，测试α5-PAM对人类大脑活动影响的研究有限，因此值得进行详细的模拟。我们利用之前建立的人类抑郁症微电路详细计算模型，该模型具有降低的SST中间神经元抑制作用和α5-PAM效应，来模拟不同抑郁症严重程度和α5-PAM剂量下单个微电路的脑电图。我们开发了机器学习模型，能够从脑电图中高精度预测最佳剂量，并恢复微电路活动和脑电图。本研究基于抑郁症严重程度的脑电图生物标志物，提供了α5-PAM给药的剂量预测模型。鉴于在活体人脑中进行上述研究存在局限性，我们开发的结果和工具将有助于将α5-PAM治疗转化为临床应用。
