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Updated: Oct 1, 2026
Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia
Published on: July 2, 2013
Effectiveness of non-pharmacological therapies for poststroke aphasia: a protocol of a systematic review and network
Mengjiao Huang1, Xinrui Huang2, Feilin Ni3
1Hospital of Zhejiang People's Armed Police, Hangzhou, People's Republic of China.
Introduction:
Poststroke aphasia is a common disabling complication following stroke, severely affecting patients' communication abilities and quality of life. Currently, various non-pharmacological therapies (eg, speech and language therapy, transcranial magnetic stimulation, computer-assisted therapy, etc) have been applied in clinical practice and show potential. However, the relative effectiveness and superiority among these interventions remain unclear. This study aims to systematically compare the effectiveness of different non-pharmacological therapies in improving language function in poststroke aphasia through a network meta-analysis and to rank their efficacy, thereby providing the best evidence for clinical decision-making.
Methods And Analysis:
The protocol will be reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. The subsequent completed systematic review and network meta-analysis will be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for network meta-analyses (an extension of the PRISMA 2020 statement) guidelines. We will systematically search PubMed, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science and China National Knowledge Infrastructure for randomised controlled trials on non-pharmacological therapies for poststroke aphasia published from inception to March 2026. Two researchers will independently conduct literature screening, data extraction and risk of bias (RoB) assessment using the Cochrane RoB tool. The primary outcome is overall language function; secondary outcomes include specific language domains and quality of life. We will employ a frequentist framework for network meta-analysis using random-effects models. The node-splitting model will assess inconsistency, and surface under the cumulative ranking curve will evaluate treatment ranking probabilities.
Ethics And Dissemination:
As this review will use only published aggregated data, ethical approval is not required. Findings will be disseminated through a peer-reviewed publication and presentations at relevant conferences.
Prospero Registration Number:
CRD420261297981.