Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol.

Ketamine shows rapid antidepressant effects in major depressive disorder, including treatment-resistant depression, but many patients do not respond, and predicting who will benefit is difficult. This study will examine computational mechanisms behind changes in the auditory mismatch negativity response after intravenous ketamine, linking them to neural causes using a hierarchical Bayesian model and a neural mass model. Thirty patients with treatment-resistant depression will undergo EEG recordings during an auditory mismatch negativity task before three of four ketamine infusions, with depression, suicidality, and anxiety assessed throughout. The findings may improve understanding of treatment response and resistance, and model parameters could enable single-patient treatment predictions.

Study at a glance

Abstract

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.