Stroke is of high morbidity and mortality, and surviving patients are often unable to take care of themselves because of severe motor dysfunction. The brain has plasticity, and makes adaptive changes after stroke, resulting in the reorganization and compensation of neural networks. However, the muscle tone of some patients will significantly increase during the recovery process, which affects the rehabilitation effect. Neuromodulation techniques such as repetitive transcranial magnetic stimulation (rTMS) have been widely used to promote brain network remodeling after stroke. The investigators attempted to evaluate the motor brain network characteristics of spastic patients by fNIRS, and used the most active brain regions as rTMS stimulation regions to evaluate the improvement effect of this individualized treatment on post-stroke spasticity.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
SINGLE
Enrollment
20
The rTMS parameters will be set according to fNIRS results.
The low-frequency rTMS to contralesional M1 will always be used.
First Affiliated Hospital of Xi'an Jiaotong University
Xi'an, Shaanxi, China
RECRUITINGmodified Ashworth scale
The range is 0-Ⅳ level, the higher the level, the higher the spasticity.
Time frame: 1 month
Fugl-Meyer Assessment of upper limb motor function
The score range is 0-66 points, the higher the score, the better the motor function of upper limb.
Time frame: 1 month
Barthel index
The score range is 0-100 points, the higher the score, the better the activities of daily living.
Time frame: 1 month
average weighted clustering coefficient
This is a brain network indicator based on graph theory. It is a measure of the local separation of the graph. The higher the average weighted clustering coefficient, the higher the degree of segregation of the brain network.
Time frame: 1 month
global efficiency
This is a brain network indicator based on graph theory. It is a measure of the local integration of the graph. The higher the global efficiency, the stronger the ability of the network to transmit information.
Time frame: 1 month
inter-density
It is a brain network indicator based on graph theory and is defined as the ratio of the actual number of connections among all possible connections between bilateral hemispheres.
Time frame: 1 month
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