ReviewInternational review of psychiatry (Abingdon, England)2017
Imaging TMS: antidepressant mechanisms and treatment optimization.
Review in International review of psychiatry (Abingdon, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- The improvement effects and mechanisms of virtual reality training on depression: a systematic review from a neurotransmitter-plasticity dual-pathway perspective.Journal of global health · 2026Pooled it
- The Effect of Single Pulse TMS on Working Memory Performance: An Online TMS And fNIRS Study.medRxiv : the preprint server for health sciences · 2026Article
- Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder: From Bench to Bedside-A Scoping Review of Neurobiological Mechanisms and Clinical Translation.Bioengineering (Basel, Switzerland) · 2026Review
- Article
- Effectiveness of Non-Invasive Brain Stimulation (NIBS) in Overeating and Increased Body Mass Index (BMI): An Umbrella Review.Clinical neuropsychiatry · 2025Review
- The Use of Transcranial Magnetic Stimulation in Attention Optimization Research: A Review from Basic Theory to Findings in Attention-Deficit/Hyperactivity Disorder and Depression.Life (Basel, Switzerland) · 2024Review
- Accelerated HF-rTMS Modifies SERT Availability in the Subgenual Anterior Cingulate Cortex: A Canine [Journal of clinical medicine · 2022Article
- Effect of transcranial magnetic stimulation on treatment effect and immune function.Saudi journal of biological sciences · 2022Article
- Maintenance treatment of transcranial magnetic stimulation (TMS) for treatment-resistant depression patients responding to acute TMS treatment.International journal of physiology, pathophysiology and pharmacology · 2020Review
- Treating refractory depression in Parkinson's disease: a meta-analysis of transcranial magnetic stimulation.Translational neurodegeneration · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
With the antidepressant efficacy of Transcranial Magnetic Stimulation well-established by several meta-analyses, there is growing interest in its mechanism of action. TMS has been shown to engage, and in some cases, normalize functional connectivity and neurotransmitter levels within networks dysfunctional in the depressed state. In this review, I will suggest candidate biomarkers, based on neuroimaging, that may be predictive of response to TMS. I will then review the effects of TMS on networks and neurotransmitter systems involved in depression. Throughout, I will also discuss how our current understanding of response predication and network engagement may be used to personalize treatment and optimize its efficacy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.