Full
Outline
To compare the functional connections and topological properties of brain networks under subjects' active, imaginary and passive hand clenching tasks based on electroencephalography technology, in order to explore new methods for brain-computer interfaces to analyze movement intentions to assist in hemiplegia rehabilitation.
The study included 15 healthy adult subjects and 19 stroke patients. The subjects collected real-time EEG in three task modes: active movement, motor imagination and passive movement. Functional connection analysis was used to compare the strength of task-state brain network functional connections between stroke groups and healthy controls; graph theory was used to compare changes in brain network topological properties between groups.
Functional connectivity analysis showed significant interactions between the stroke group and healthy controls in the frontal, parietal, and occipital lobes. The sensorimotor network of stroke patients showed higher connection strength than that of healthy subjects. Brain network graph theory analysis showed that the clustering coefficient of the stroke group was significantly lower than that of the healthy control group, the characteristic path length was generally higher than that of the healthy control group, and the small-world coefficient was significantly lower than that of the healthy control group.
The recovery of upper limb function in stroke patients is related to the compensatory adjustment of brain network functional connections, which specifically includes strengthening the connection strength of the sensorimotor network related to the patient's movement sensation and the high-level motor cortex such as the fronto-parietal network. Graph theory analysis shows that the brain after stroke has a lower clustering coefficient and a longer characteristic path length, verifying that the structure and function of the brain network are destroyed after stroke, the global efficiency is significantly reduced, the information transmission efficiency decreases, and a transformation to a random network is observed in the resting state brain network.
62
10
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
