Martin Josh, Gholamali Nezhad Fatemeh, Rueda Alice, Lee Gyu Hee, Charlton Colleen E, Soltanzadeh Milad, Ladha Karim S, Krishnan Sridhar, Diaconescu Andreea O, Bhat Venkat
Interventional Psychiatry Program, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada.
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
PLoS One. 2024 Aug 8;19(8):e0308413. doi: 10.1371/journal.pone.0308413. eCollection 2024.
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.
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.
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.
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.
Clinicaltrials.gov NCT05464264. Registered June 24, 2022.
氯胺酮因其对重度抑郁症患者（包括难治性抑郁症，TRD）的快速疗效，最近备受关注。尽管氯胺酮在治疗抑郁症方面取得了令人鼓舞的结果，但仍有相当数量的患者对该治疗无反应，预测谁将从中受益仍是一项挑战。虽然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，但决定为何有些患者有反应而有些患者没有反应的精确机制仍不清楚。
本研究旨在了解静脉注射氯胺酮治疗后听觉失配负波（MMN）反应变化背后的计算机制。此外，我们旨在将这些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进行个体治疗预测。
这是一项对30例接受静脉氯胺酮治疗的TRD患者的前瞻性研究。在4次氯胺酮输注中的3次之前，当患者完成听觉MMN任务时记录脑电图。在整个研究过程以及最后一次氯胺酮输注后一周，评估抑郁、自杀倾向和焦虑情况。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（一种分层贝叶斯模型）对听觉MMN的变化进行建模。此外，我们将采用基于电导的电生理数据神经群体模型，将这些计算机制与其神经原因联系起来。
本研究的结果可能会增进对TRD患者氯胺酮治疗反应和抵抗机制的理解。通过将计算模型拟合到脑电图记录中获得的参数可能有助于单患者治疗预测，这可以提供临床上有用的预后信息。
Clinicaltrials.gov NCT05464264。2022年6月24日注册。
