Electrophysiological correlates and predictors of the antidepressant response to repeated ketamine infusions in treatment-resistant depression

Spectral signatures of psilocybin, lysergic acid diethylamide (LSD) and ketamine in healthy volunteers and persons with major depressive disorder and treatment-resistant depression: A systematic review

BACKGROUND

Electrophysiologic measures provide an opportunity to inform mechanistic models and possibly biomarker prediction of response. Serotonergic psychedelics (SPs) (i.e., psilocybin, lysergic acid diethylamide (LSD)) and ketamine represent new investigational and established treatments in mood disorders respectively. There is a need to better characterize the mechanism of action of these agents.

METHODS

We conducted a systematic review investigating the spectral signatures of psilocybin, LSD, and ketamine in persons with major depressive disorder (MDD), treatment-resistant depression (TRD), and healthy controls.

RESULTS

Ketamine and SPs are associated with increased theta power in persons with depression. Ketamine and SPs are also associated with decreased spectral power in the alpha, beta and delta bands in healthy controls and persons with depression. When administered with SPs, theta power was increased in persons with MDD when administered with SPs. Ketamine is associated with increased gamma band power in both healthy controls and persons with MDD.

LIMITATIONS

The studies included in our review were heterogeneous in their patient population, exposure, dosing of treatment and devices used to evaluate EEG and MEG signatures. Our results were extracted entirely from persons who were either healthy volunteers or persons with MDD or TRD.

CONCLUSIONS

Extant literature evaluating EEG and MEG spectral signatures indicate that ketamine and SPs have reproducible effects in keeping with disease models of network connectivity. Future research vistas should evaluate whether observed spectral signatures can guide further discovery of therapeutics within the psychedelic and dissociative classes of agents, and its prediction capability in persons treated for depression.

Mechanisms of Sustained Increases in γ Power Post-Ketamine in a Computational Model of the Hippocampal CA3: Implications for Ketamine's Antidepressant Mechanism of Action

Subanaesthetic doses of ketamine increase γ oscillation power in neural activity measured using electroencephalography (EEG), and this effect lasts several hours after ketamine administration. The mechanisms underlying this effect are unknown. Using a computational model of the hippocampal cornu ammonis 3 (CA3) network, which is known to reproduce ketamine's acute effects on γ power, we simulated the plasticity of glutamatergic synapses in pyramidal cells to test which of the following hypotheses would best explain this sustained γ power: the direct inhibition hypothesis, which proposes that increased γ power post-ketamine administration may be caused by the potentiation of recurrent collateral synapses, and the disinhibition hypothesis, which proposes that potentiation affects synapses from both recurrent and external inputs. Our results suggest that the strengthening of external connections to pyramidal cells is able to account for the sustained γ power increase observed post-ketamine by increasing the overall activity of and synchrony between pyramidal cells. The strengthening of recurrent pyramidal weights, however, would cause an additional phase shifted voltage increase that ultimately reduces γ power due to partial cancellation. Our results therefore favor the disinhibition hypothesis for explaining sustained γ oscillations after ketamine administration.

Storm on predictive brain: A neurocomputational account of ketamine antidepressant effect

For the past decade, ketamine, an N-methyl-D-aspartate receptor (NMDAr) antagonist, has been considered a promising treatment for major depressive disorder (MDD). Unlike the delayed effect of monoaminergic treatment, ketamine may produce fast-acting antidepressant effects hours after a single administration at subanesthetic dose. Along with these antidepressant effects, it may also induce transient dissociative (disturbing of the sense of self and reality) symptoms during acute administration which resolve within hours. To understand ketamine's rapid-acting antidepressant effect, several biological hypotheses have been explored, but despite these promising avenues, there is a lack of model to understand the timeframe of antidepressant and dissociative effects of ketamine. In this article, we propose a neurocomputational account of ketamine's antidepressant and dissociative effects based on the Predictive Processing (PP) theory, a framework for cognitive and sensory processing. PP theory suggests that the brain produces top-down predictions to process incoming sensory signals, and generates bottom-up prediction errors (PEs) which are then used to update predictions. This iterative dynamic neural process would relies on N-methyl-D-aspartate (NMDAr) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic receptors (AMPAr), two major component of the glutamatergic signaling. Furthermore, it has been suggested that MDD is characterized by over-rigid predictions which cannot be updated by the PEs, leading to miscalibration of hierarchical inference and self-reinforcing negative feedback loops. Based on former empirical studies using behavioral paradigms, neurophysiological recordings, and computational modeling, we suggest that ketamine impairs top-down predictions by blocking NMDA receptors, and enhances presynaptic glutamate release and PEs, producing transient dissociative symptoms and fast-acting antidepressant effect in hours following acute administration. Moreover, we present data showing that ketamine may enhance a delayed neural plasticity pathways through AMPAr potentiation, triggering a prolonged antidepressant effect up to seven days for unique administration. Taken together, the two sides of antidepressant effects with distinct timeframe could constitute the keystone of antidepressant properties of ketamine. These PP disturbances may also participate to a ketamine-induced time window of mental flexibility, which can be used to improve the psychotherapeutic process. Finally, these proposals could be used as a theoretical framework for future research into fast-acting antidepressants, and combination with existing antidepressant and psychotherapy.

A synopsis of multitarget therapeutic effects of anesthetics on depression

Depression is a profound mental disorder that dampens the mood and undermines volition, which exhibited an increased incidence over the years. Although drug-based interventions remain the primary approach for depression treatment, the available medications still can't satisfy the patients. In recent years, the newly discovered therapeutic targets such as N-methyl-D-aspartate (NMDA) receptor, α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) receptor, and tyrosine kinase B (TrkB) have brought new breakthroughs in the development of antidepressant drugs. Moreover, it has come to light that certain anesthetics possess pharmacological mechanisms intricately linked to the aforementioned therapeutic targets for depression. At present, numerous preclinical and clinical studies have explored the therapeutic effects of anesthetic drugs such as ketamine, isoflurane, N2O, and propofol, on depression. These investigations suggested that these drugs can swiftly ameliorate patients' depression symptoms and engender long-term effects. In this paper, we provide a comprehensive review of the research progress and potential molecular mechanisms of various anesthetic drugs for depression treatment. By shedding light on this subject, we aim to facilitate the development and clinical implementation of new antidepressant drugs based on anesthetic medications.

Neural complexity EEG biomarkers of rapid and post-rapid ketamine effects in late-life treatment-resistant depression: a randomized control trial

Ketamine is an effective intervention for treatment-resistant depression (TRD), including late-in-life (LL-TRD). The proposed mechanism of antidepressant effects of ketamine is a glutamatergic surge, which can be measured by electroencephalogram (EEG) gamma oscillations. Yet, non-linear EEG biomarkers of ketamine effects such as neural complexity are needed to capture broader systemic effects, represent the level of organization of synaptic communication, and elucidate mechanisms of action for treatment responders. In a secondary analysis of a randomized control trial, we investigated two EEG neural complexity markers (Lempel-Ziv complexity [LZC] and multiscale entropy [MSE]) of rapid (baseline to 240 min) and post-rapid ketamine (24 h and 7 days) effects after one 40-min infusion of IV ketamine or midazolam (active control) in 33 military veterans with LL-TRD. We also studied the relationship between complexity and Montgomery-Åsberg Depression Rating Scale score change at 7 days post-infusion. We found that LZC and MSE both increased 30 min post-infusion, with effects not localized to a single timescale for MSE. Post-rapid effects of reduced complexity with ketamine were observed for MSE. No relationship was observed between complexity and reduction in depressive symptoms. Our findings support the hypothesis that a single sub-anesthetic ketamine infusion has time-varying effects on system-wide contributions to the evoked glutamatergic surge in LL-TRD. Further, changes to complexity were observable outside the time-window previously shown for effects on gamma oscillations. These preliminary results have clinical implications in providing a functional marker of ketamine that is non-linear, amplitude-independent, and represents larger dynamic properties, providing strong advantages over linear measures in highlighting ketamine's effects.

Brain-based correlates of antidepressant response to ketamine: a comprehensive systematic review of neuroimaging studies

Ketamine is an effective antidepressant, but there is substantial variability in patient response and the precise mechanism of action is unclear. Neuroimaging can provide predictive and mechanistic insights, but findings are limited by small sample sizes. This systematic review covers neuroimaging studies investigating baseline (pre-treatment) and longitudinal (post-treatment) biomarkers of responses to ketamine. All modalities were included. We performed searches of five electronic databases (from inception to April 26, 2022). 69 studies were included (with 1751 participants). There was substantial methodological heterogeneity and no well replicated biomarker. However, we found convergence across some significant results, particularly in longitudinal biomarkers. Response to ketamine was associated with post-treatment increases in gamma power in frontoparietal regions in electrophysiological studies, post-treatment increases in functional connectivity within the prefrontal cortex, and post-treatment increases in the functional activation of the striatum. Although a well replicated neuroimaging biomarker of ketamine response was not identified, there are biomarkers that warrant further investigation.

Ketamine and serotonergic psychedelics: An update on the mechanisms and biosignatures underlying rapid-acting antidepressant treatment

The discovery of ketamine as a rapid-acting antidepressant spurred significant research to understand its underlying mechanisms of action and to identify other novel compounds that may act similarly. Serotonergic psychedelics (SPs) have shown initial promise in treating depression, though the challenge of conducting randomized controlled trials with SPs and the necessity of long-term clinical observation are important limitations. This review summarizes the similarities and differences between the psychoactive effects associated with both ketamine and SPs and the mechanisms of action of these compounds, with a focus on the monoaminergic, glutamatergic, gamma-aminobutyric acid (GABA)-ergic, opioid, and inflammatory systems. Both molecular and neuroimaging aspects are considered. While their main mechanisms of action differ-SPs increase serotonergic signaling while ketamine is a glutamatergic modulator-evidence suggests that the downstream mechanisms of action of both ketamine and SPs include mechanistic target of rapamycin complex 1 (mTORC1) signaling and downstream GABAA receptor activity. The similarities in downstream mechanisms may explain why ketamine, and potentially SPs, exert rapid-acting antidepressant effects. However, research on SPs is still in its infancy compared to the ongoing research that has been conducted with ketamine. For both therapeutics, issues with regulation and proper controls should be addressed before more widespread implementation. This article is part of the Special Issue on "Ketamine and its Metabolites".

A Predictive Coding Framework for Understanding Major Depression

Predictive coding models of brain processing propose that top-down cortical signals promote efficient neural signaling by carrying predictions about incoming sensory information. These "priors" serve to constrain bottom-up signal propagation where prediction errors are carried via feedforward mechanisms. Depression, traditionally viewed as a disorder characterized by negative cognitive biases, is associated with disrupted reward prediction error encoding and signaling. Accumulating evidence also suggests that depression is characterized by impaired local and long-range prediction signaling across multiple sensory domains. This review highlights the electrophysiological and neuroimaging evidence for disrupted predictive processing in depression. The discussion is framed around the manner in which disrupted generative predictions about the sensorium could lead to depressive symptomatology, including anhedonia and negative bias. In particular, the review focuses on studies of sensory deviance detection and reward processing, highlighting research evidence for both disrupted generative predictions and prediction error signaling in depression. The role of the monoaminergic and glutamatergic systems in predictive coding processes is also discussed. This review provides a novel framework for understanding depression using predictive coding principles and establishes a foundational roadmap for potential future research.