背景:氯胺酮对重度抑郁症患者的快速影响最近引起了相当大的关注,包括难治性抑郁症(TRD)。尽管氯胺酮在治疗抑郁症方面有希望的结果,相当数量的患者对治疗没有反应,预测谁将受益仍然是一个挑战。尽管已知其抗抑郁作用与其作为N-甲基-D-天冬氨酸(NMDA)受体拮抗剂的作用有关,确定某些患者有反应而另一些患者有反应的确切机制仍不清楚.
目的:本研究旨在了解静脉注射氯胺酮治疗后听觉失配负性(MMN)反应变化的潜在计算机制。此外,我们的目标是将计算机制与其潜在的神经原因联系起来,并使用神经计算模型的参数来进行个体治疗预测。
方法:这是一项对30名接受静脉氯胺酮治疗的TRD患者进行的前瞻性研究。在4次氯胺酮输注3次之前,当患者完成听觉MMN任务时,将记录EEG。抑郁症,自杀,和焦虑将在整个研究和最后一次氯胺酮输注后一周进行评估。为了将氯胺酮对MMN的影响转化为计算机制,我们将使用分层高斯滤波器对听觉MMN的变化进行建模,分层贝叶斯模型。此外,我们将采用电生理数据的基于电导的神经质量模型,将这些计算机制与它们的神经原因联系起来。
结论:这项研究的结果可能会提高对TRD患者对氯胺酮治疗的反应和耐药机制的理解。从将计算模型拟合到EEG记录获得的参数可以促进单患者治疗预测,这可以提供临床有用的预后信息。
背景:Clinicaltrials.govNCT05464264。2022年6月24日注册。
BACKGROUND: Ketamine has recently attracted considerable attention for its rapid effects on patients with major depressive disorder, including treatment-resistant depression (TRD). Despite ketamine\'s promising results in treating depression, a significant number of patients do not respond to the treatment, and predicting who will benefit remains a challenge. Although its antidepressant effects are known to be linked to its action as an antagonist of the N-methyl-D-aspartate (NMDA) receptor, the precise mechanisms that determine why some patients respond and others do not are still unclear.
OBJECTIVE: This
study aims to understand the computational mechanisms underlying changes in the auditory mismatch negativity (MMN) response following treatment with intravenous ketamine. Moreover, we aim to link the computational mechanisms to their underlying neural causes and use the parameters of the neurocomputational model to make individual treatment predictions.
METHODS: This is a prospective
study of 30 patients with TRD who are undergoing intravenous ketamine therapy. Prior to 3 out of 4 ketamine infusions, EEG will be recorded while patients complete the auditory MMN task. Depression, suicidality, and anxiety will be assessed throughout the
study and a week after the last ketamine infusion. To translate the effects of ketamine on the MMN to computational mechanisms, we will model changes in the auditory MMN using the hierarchical Gaussian filter, a hierarchical Bayesian model. Furthermore, we will employ a conductance-based neural mass model of the electrophysiological data to link these computational mechanisms to their neural causes.
CONCLUSIONS: The findings of this
study may improve understanding of the mechanisms underlying response and resistance to ketamine treatment in patients with TRD. The parameters obtained from fitting computational models to EEG recordings may facilitate single-patient treatment predictions, which could provide clinically useful prognostic information.
BACKGROUND: Clinicaltrials.gov NCT05464264. Registered June 24, 2022.