Potential predictors of depressive relapse following repetitive Transcranial Magnetic Stimulation: A systematic review

Trajectory of changes in depressive symptoms after acute repetitive transcranial magnetic stimulation: A meta-analysis of follow-up effects

BACKGROUND

The follow-up effect after acute repetitive transcranial magnetic stimulation (rTMS) for major depressive episodes remains unclear. Furthermore, the benefits of maintenance rTMS are poorly understood.

AIM

To investigate the trajectory of changes in depressive symptoms after acute rTMS and effects of maintenance rTMS during this period.

METHOD

This meta-analysis (PROSPERO: CRD42022374077) searched major databases up to October 1, 2022. Treatment outcome was depressive scores collected at least 3 months after the end of an acute rTMS course for depression. We extracted data at different time points after acute rTMS and categorized by whether maintenance rTMS was performed. A single-stage random-effects dose-response meta-analysis was undertaken to model the nonlinear relationships. Effect sizes were calculated as standardized mean differences (SMDs) with 95% confidence intervals (CIs).

RESULTS

24 eligible studies comprising 911 total patients-225 of whom received maintenance rTMS-were included. Maintenance rTMS contributed to relative stability in patients' mood symptoms during the first 5 months (SMD [95% CI]: 3rd month, -0.10 [-0.30 to 0.10]; 5th month, 0.00 [-0.55 to 0.55]), with heterogeneity characterized as low to moderate. Further analysis revealed that maintenance rTMS performed monthly or more frequently provided sustained benefits for up to 6-12 months. Conversely, patients without maintenance rTMS had moderate to high heterogeneity, although the change in mean mood symptom scores during the 12-month follow-up was also minor (6th month, 0.03 [-0.51 to 0.56]; 12th month, 0.10 [-0.59 to 0.79]).

CONCLUSION

Maintenance rTMS might keep patients' mood relatively stable for up to 5 months after acute rTMS. Monthly or more frequent maintenance rTMS offers greater benefits.

Novel and emerging treatments for major depression

Depression is common, costly, debilitating, and associated with increased risk of suicide. It is one of the leading global public health problems. Although existing available pharmacological treatments can be effective, their onset of action can take up to 6 weeks, side-effects are common, and recovery can require treatment with multiple different agents. Although psychosocial interventions might also be recommended, more effective treatments than those currently available are needed for people with moderate or severe depression. In the past 10 years, treatment trials have developed and tested many new targeted interventions. In this Review, we assess novel and emerging biological treatments for major depressive disorder, evaluate their putative brain and body mechanisms, and highlight how close each might be to clinical use.

Evidence and expert consensus based German guidelines for the use of repetitive transcranial magnetic stimulation in depression

INTRODUCTION

Non-invasive brain stimulation techniques such as repetitive transcranial magnetic stimulation (rTMS) offer a promising alternative to psychotherapeutic and pharmacological treatments for depression. This paper aims to present a practical guide for its clinical implementation based on evidence from the literature as well as on the experience of a group of leading German experts in the field.

METHODS

The current evidence base for the use of rTMS in depression was examined via review of the literature. From the evidence and from clinical experience, recommendations for the use of rTMS in clinical practice were derived. All members of the of the German Society for Brain Stimulation in Psychiatry and all members of the sections Clinical Brain Stimulation and Experimental Brain Stimulation of the German Society for Psychiatry, Psychotherapy, Psychosomatics and Mental Health were invited to participate in a poll on whether they consent with the recommendations.

FINDINGS

Among rTMS experts, a high consensus rate could be identified for clinical practice concerning the setting and the technical parameters of rTMS treatment in depression, indications and contra-indications, the relation of rTMS to other antidepressive treatment modalities and the frequency and management of side effects.

The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Cognitive and emotional predictors of real versus sham repetitive transcranial magnetic stimulation treatment response in methamphetamine use disorder

BACKGROUND AND AIMS

Repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (DLPFC) can effectively reduce cravings in methamphetamine use disorder (MUD). However, a considerable group still fails to respond. Cognitive and emotional disturbance, as well as impulsive features, are widespread in patients with MUD and might mediate the treatment response of rTMS. The purpose of this study is to figure out whether these variables can help predicting patients' responses to rTMS treatment.

METHODS

Ninety-seven patients with severe MUD and thirty-one gender- and age-matched healthy subjects were included. Patients were randomized to receive 20 sessions of real or sham rTMS. Intermittent theta burst protocols (iTBS) or sham iTBS were applied every weekday over the DLPFC for 20 daily sessions. Both groups received regular treatment. Craving induced by drug-related cue was measured before and after stimulation. Cognition was evaluated by using the CogState Battery. Baseline characteristics were collected through the Addiction Severity Index, Patient Health Questionnaire-9, General Anxiety Disorder Scale-7, and Barrett Impulsivity Scale-11.

RESULTS

Results showed that patients with MUD have worse spatial working memory, problem-solving ability, as well as depression and anxiety symptoms compared with healthy controls. Cognition and emotion differed between responders (craving decrease ≥60%) and non-responders in real rTMS group but not in the sham group. Better cognitive and emotional functions means that patients have higher possibility for better response to real rTMS treatment.

CONCLUSIONS

This study suggests that cognitive, emotional and impulsive features could be used to predict the prospective treatment responses of rTMS in patients with MUD.