The effects of high-frequency repeitive transcranial magnetic stimulation of the cerebellum on swallowing neural networks
10.3760/cma.j.cn421666-20231113-00910
- VernacularTitle:小脑高频重复经颅磁刺激对吞咽神经网络影响的功能性磁共振成像观察
- Author:
Wei LI
1
;
Yang ZHAO
;
Guangbin WANG
;
Xianguo MENG
Author Information
1. 山东第一医科大学，济南 250117
- Publication Type:Journal Article
- Keywords:
Swallowing;
Cerebellum;
Functional magnetic resonance imaging;
Transcranial magnetic stimulation
- From:
Chinese Journal of Physical Medicine and Rehabilitation
2025;47(5):398-402
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore any effect of high-frequency repetitive transcranial magnetic stimulation (rTMS) of the left cerebellar hemisphere on the swallowing neural network using functional magnetic resonance imaging (fMRI).Methods:Thirty healthy volunteers were recruited and subjected to 10Hz rTMS of the left cerebellar hemisphere. Before and after the stimulation, fMRI was performed. The amplitude of the motor evoked potential (MEP) of the mylohyoid muscle, the amplitude of its low-frequency fluctuations (ALFFs), their fractional amplitude (fALFF), and their regional homogeneity (ReHo) were observed.Results:After the intervention the MEP amplitude of both mylohyoid muscles had increased significantly. The ALFF and fALFF values in the left cerebellar region, occipital lobe, prefrontal lobe, cuneus lobe, anterior central gyrus and posterior central gyrus had increased significantly. And the average ReHo values of the left cerebellum, occipital lobe, parietal lobe, right anterior central gyrus and posterior central gyrus had also significantly increased.Conclusions:The stimulation studied can significantly enhance the excitability of bilateral swallowing cortical tracts, as well as the neural activities of the anterior central gyrus, posterior central gyrus, occipital lobe, cuneus lobe, temporal lobe, inferior parietal gyrus, right premotor cortex, and right dorsolateral prefrontal cortex. That suggests that the cerebellum plays an important role in the swallowing neural network.
