Evidence map›Paper›PMID 29736128›Full record

ReviewShanghai archives of psychiatry2018

Mechanism of Repetitive Transcranial Magnetic Stimulation for Depression.

Zhengwu Peng, Cuihong Zhou, Shanshan Xue, Jie Bai, Shoufen Yu, Xiaosa Li, Huaning Wang, Qingrong Tan

Registry-linked trialAbstract readReview
In one paragraph

Review in Shanghai archives of psychiatry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04441008 (Radical aiTBS Protocol for Intractable Depression), which is not on this map. Cited by 48 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT04441008 nanot yet recruitingnot on this mapstarted 2026, after this paper: background citation

Radical aiTBS Protocol for Intractable Depression

TypeinterventionalSponsorUniversity of IowaRan2026 to 2030Enrolled50ConditionsMajor Depressive Disorder, SuicideArmsAccelerated Intermittent Theta Burst (aiTBS), Electroconvulsive therapy (ECT)
3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zhengwu PengDepartment of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Cuihong ZhouDepartment of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Shanshan XueDepartment of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Jie BaiDepartment of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Shoufen YuDepartment of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Xiaosa LiDepartment of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Huaning WangDepartment of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Qingrong TanDepartment of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Depressive disorder is one of the most common mental health problems currently. However, the mechanism-based treatments for this disorder remain elusive. Repetitive transcranial magnetic stimulation (rTMS), a non-invasive procedure that could stimulate electrical activity by a pulsed magnetic field in the brain, is considered to be an effective treatment for depression. Here, we review the main findings from both clinical and basic research on rTMS for depression, including its antidepressant efficacy, basic principles, as well as its ability to regulate neural circuits, neurotransmitters and brain networks, neurogenesis in hippocampus, and synaptic, and molecular pathways.

Indexed as

DepressionNeurophysiologyRepetitiveTranscranial Magnetic Stimulation

Identifiers

PMID29736128
PMCPMC5936045

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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.