Low-frequency repetitive transcranial magnetic stimulation for patients with aphasia after stoke: A meta-analysis
Yi Li , Yun Qu, Mengwei Yuan , Tianhui Du
Department of Rehabilitation Medicine, West China Hospital, Sichuan Provincial Key Laboratory of Rehabilitation Medicine, Sichuan University, Chengdu, Sichuan, People's Republic of China, China
DOI: 10.2340/16501977-1988
Abstract
Objective: To perform a meta-analysis of studies investigating the effects of low-frequency repetitive transcranial magnetic stimulation on post-stroke aphasia.
Data sources: Studies were identified by performing a search of electronic databases (MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Knowledge) for articles published until June 2014.
Study selection: Randomized controlled trials (RCTs) reporting treatments with low-frequency repetitive transcranial magnetic stimulation in patients with post-stroke aphasia were included. The outcomes included naming, repetition, comprehension, changes in brain excitability, and adverse events.
Data extraction: Two independent reviewers extracted the data. Study quality was evaluated with the PEDro scale.
Data analysis: Of the 879 articles identified, 4 RCTs were included in the final analysis. Data synthesis showed that low-frequency repetitive transcranial magnetic stimulation was beneficial for post-stroke patients in terms of naming (standard mean difference (SMD) 0. 51; 95% confidence interval (95% CI) 0. 16–0. 86) and changes in brain excitability (7. 6 ± 33. 55; 95% CI –10. 7–26. 20). However, the changes in repetition (SMD 0. 31; 95% CI –0. 04–0. 65) and comprehension (SMD 0. 31; 95% CI –0. 14–0. 75) after stimulation were not significant. No adverse effects were reported. The included studies were of high methodological quality.
Conclusion: These findings indicate that low-frequency repetitive transcranial magnetic stimulation is an effective treatment for recovery of naming. In addition, this treatment favours reorganization of the left-hemispheric language networks.
Lay Abstract
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