Neurophysiological Effect of Ketamine on Prefrontal Cortex in Treatment-Resistant Depression: A Combined Transcranial Magnetic Stimulation-Electroencephalography Study

Transcranial Magnetic Stimulation-Electroencephalography for Biomarker Discovery in Psychiatry

Current diagnosis and treatment of psychiatric illnesses are still based on behavioral observations and self-reports, commonly leading to prolonged untreated illness. Biological markers (biomarkers) may offer an opportunity to revolutionize clinical psychiatry practice by helping provide faster and potentially more effective therapies. Transcranial magnetic stimulation concurrent with electroencephalography (TMS-EEG) is a noninvasive brain mapping methodology that can assess the functions and dynamics of specific brain circuitries in awake humans and aid in biomarker discovery. This article provides an overview of TMS-EEG-based biomarkers that may hold potential in psychiatry. The methodological readiness of the TMS-EEG approach and steps in the validation of TMS-EEG biomarkers for clinical utility are discussed. Biomarker discovery with TMS-EEG is in the early stages, and several validation steps are still required before clinical implementations are realized. Thus far, TMS-EEG predictors of response to magnetic brain stimulation treatments in particular have shown promise for translation to clinical practice. Larger-scale studies can confirm validation followed by biomarker-informed trials to assess added value compared to existing practice.

Transcranial magnetic stimulation and ketamine: implications for combined treatment in depression

Drug-resistant mental disorders, particularly treatment-resistant depression, pose a significant medical and social problem. To address this challenge, modern psychiatry is constantly exploring the use of novel treatment methods, including biological treatments, such as transcranial magnetic stimulation (TMS), and novel rapid-acting antidepressants, such as ketamine. While both TMS and ketamine demonstrate high effectiveness in reducing the severity of depressive symptoms, some patients still do not achieve the desired improvement. Recent literature suggests that combining these two methods may yield even stronger and longer-lasting results. This review aims to consolidate knowledge in this area and elucidate the potential mechanisms of action underlying the increased efficacy of combined treatment, which would provide a foundation for the development and optimization of future treatment protocols.

Ketamine and Zinc: Treatment of Anorexia Nervosa Via Dual NMDA Receptor Modulation

Anorexia nervosa is a disorder associated with serious adverse health outcomes, for which there is currently considerable treatment ineffectiveness. Characterised by restrictive eating behaviours, distorted body image perceptions and excessive physical activity, there is growing recognition anorexia nervosa is associated with underlying dysfunction in excitatory and inhibitory neurometabolite metabolism and signalling. This narrative review critically explores the role of N-methyl-D-aspartate receptor-mediated excitatory and inhibitory neurometabolite dysfunction in anorexia nervosa and its associated biomarkers. The existing magnetic resonance spectroscopy literature in anorexia nervosa is reviewed and we outline the brain region-specific neurometabolite changes that have been reported and their connection to anorexia nervosa psychopathology. Considering the proposed role of dysfunctional neurotransmission in anorexia nervosa, the potential utility of zinc supplementation and sub-anaesthetic doses of ketamine in normalising this is discussed with reference to previous research in anorexia nervosa and other neuropsychiatric conditions. The rationale for future research to investigate the combined use of low-dose ketamine and zinc supplementation to potentially extend the therapeutic benefits in anorexia nervosa is subsequently explored and promising biological markers for assessing and potentially predicting treatment response are outlined.

Source-based artifact-rejection techniques for TMS-EEG

Neuronal electroencephalography (EEG) signals arise from the cortical postsynaptic currents. Due to the conductive properties of the head, these neuronal sources produce relatively smeared spatial patterns in EEG. We can model these topographies to deduce which signals reflect genuine TMS-evoked cortical activity and which data components are merely noise and artifacts. This review will concentrate on two source-based artifact-rejection techniques developed for TMS-EEG data analysis, signal-space-projection-source-informed reconstruction (SSP-SIR), and the source-estimate-utilizing noise-discarding algorithm (SOUND). The former method was designed for rejecting TMS-evoked muscle artifacts, while the latter was developed to suppress noise signals from EEG and magnetoencephalography (MEG) in general. We shall cover the theoretical background for both methods, but most importantly, we will describe some essential practical perspectives for using these techniques effectively. We demonstrate and explain what approaches produce the most reliable inverse estimates after cleaning the data or how to perform non-biased comparisons between cleaned datasets. All noise-cleaning algorithms compromise the signals of interest to a degree. We elaborate on how the source-based methods allow objective quantification of the overcorrection. Finally, we consider possible future directions. While this article concentrates on TMS-EEG data analysis, many theoretical and practical aspects, presented here, can be readily applied in other EEG/MEG applications. Overall, the source-based cleaning methods provide a valuable set of TMS-EEG preprocessing tools. We can objectively evaluate their performance regarding possible overcorrection. Furthermore, the overcorrection can always be taken into account to compare cleaned datasets reliably. The described methods are based on current electrophysiological and anatomical understanding of the head and the EEG generators; strong assumptions of the statistical properties of the noise and artifact signals, such as independence, are not needed.