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

Abstract

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.

Conclusion: 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.

Trial registration: Clinicaltrials.gov NCT05464264. Registered June 24, 2022.

Fig 1. Study design.

Fig 2. Auditory MMN task.

(A) The auditory MMN task consists of a train of alternating tones of different frequencies, and the probability of the tones is systematically varied with a given probabilistic schedule including alternating stable and volatile phases. (B) Three-level hierarchical Gaussian filter (HGF) binary perceptual model, a Hierarchical Bayesian Model, is used to model the effect of ketamine on MMN.

Fig 3. Predicting antidepressant ketamine effects in TRD using a conductance-based neural mass model.

This flowchart provides a step-by-step approach to modeling ketamine effects of perceptual inference using the auditory MMN paradigm and EEG recordings. 1) The auditory MMN, which occurs when a sequence of low tones (predictable stimulus or standard) is unexpectedly interrupted by a high tone (unpredictable stimulus or deviant). 2) The second step includes specifying the generative model, which embodies a probabilistic forward mapping from hidden brain states to observed auditory MMN. 3) The model can be inverted and applied to data for inferring underlying individual pathophysiology. (3-population neural model, with inhibitory interneuron, spiny stellate cell, and pyramidal cell populations in layer IV cortical column drawn using Adobe Illustrator). For illustration purposes, intrinsic glutamatergic connections mediated by the AMPA and NMDA receptors are grouped. 4) The utility of these models for detecting physiologically defined subgroups will be tested in TRD patients.