Suppr超能文献

中风后偏瘫患者腕关节伸展的时变网络

The time-varying networks of the wrist extension in post-stroke hemiplegic patients.

作者信息

Li Fali, Jiang Lin, Zhang Yangsong, Huang Dongfeng, Wei Xijun, Jiang Yuanling, Yao Dezhong, Xu Peng, Li Hai

机构信息

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China.

School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China.

出版信息

Cogn Neurodyn. 2022 Aug;16(4):757-766. doi: 10.1007/s11571-021-09738-2. Epub 2021 Nov 2.

Abstract

UNLABELLED

Hemiplegia is a common dysfunction caused by the brain stroke and leads to movement disability. Although the lateralization of movement-related potential, the event-related desynchronization, and more complicated inter-regional information coupling have been investigated, seldom studies have focused on investigating the dynamic information exchanging among multiple brain regions during motor execution for post-stroke hemiplegic patients. With high temporal-resolution electroencephalogram (EEG), the time-varying network is able to reflect the dynamical complex network modalities corresponding to the movements at a millisecond level. In our present study, the wrist extension experiment was designed, along with related EEG datasets being collected. Thereafter, the corresponding time-varying networks underlying the wrist extension were accordingly constructed by adopting the adaptive directed transfer function and then statistically explored, to further uncover the dynamic network deficits (i.e., motor dysfunction) in post-stroke hemiplegic patients. Results of this study found the effective connectivity between the stroked motor area and other areas decreased in patients when compared to healthy controls; on the contrary, the enhanced connectivity between non-stroked motor areas and other areas, especially the frontal and parietal-occipital lobes, were further identified for patients during their accomplishing the designed wrist extension, which might dynamically compensate for the deficited patients' motor behaviors. These findings not only helped deepen our knowledge of the mechanism underlying the patients' motor behaviors, but also facilitated the real-time strategies for clinical therapy of brain stroke, as well as providing a reliable biomarker to predict the future rehabilitation.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11571-021-09738-2.

摘要

未标注

偏瘫是由脑卒引起的常见功能障碍,会导致运动残疾。尽管已经对与运动相关电位的偏侧化、事件相关去同步化以及更复杂的区域间信息耦合进行了研究,但很少有研究关注中风后偏瘫患者运动执行过程中多个脑区之间的动态信息交换。利用高时间分辨率脑电图(EEG),时变网络能够在毫秒级别反映与运动相对应的动态复杂网络模式。在我们目前的研究中,设计了手腕伸展实验,并收集了相关的EEG数据集。此后,通过采用自适应定向传递函数构建了手腕伸展相应的时变网络,并进行了统计探索,以进一步揭示中风后偏瘫患者的动态网络缺陷(即运动功能障碍)。本研究结果发现,与健康对照组相比,患者中风侧运动区与其他区域之间的有效连接减少;相反,在患者完成设计的手腕伸展过程中,进一步发现非中风侧运动区与其他区域,特别是额叶和顶枕叶之间的连接增强,这可能会动态补偿患者受损的运动行为。这些发现不仅有助于加深我们对患者运动行为潜在机制的认识,还促进了脑卒临床治疗的实时策略,以及提供了一个可靠的生物标志物来预测未来的康复情况。

补充信息

在线版本包含可在10.1007/s11571-021-09738-2获取的补充材料。

相似文献

1
The time-varying networks of the wrist extension in post-stroke hemiplegic patients.
Cogn Neurodyn. 2022 Aug;16(4):757-766. doi: 10.1007/s11571-021-09738-2. Epub 2021 Nov 2.
2
Time-Varying Effective Connectivity for Describing the Dynamic Brain Networks of Post-stroke Rehabilitation.
Front Aging Neurosci. 2022 May 24;14:911513. doi: 10.3389/fnagi.2022.911513. eCollection 2022.
3
Integrated technology for evaluation of brain function and neural plasticity.
Phys Med Rehabil Clin N Am. 2004 Feb;15(1):263-306. doi: 10.1016/s1047-9651(03)00124-4.
5
Effect of real-time cortical feedback in motor imagery-based mental practice training.
NeuroRehabilitation. 2014;34(2):355-63. doi: 10.3233/NRE-131039.
7
Manual mobilization of the wrist: a pilot study in rehabilitation of patients with a chronic hemiplegic hand post-stroke.
J Hand Ther. 2014 Jul-Sep;27(3):209-15; quiz 216. doi: 10.1016/j.jht.2013.12.011. Epub 2014 Jan 2.
8
EEG Changes in Time and Time-Frequency Domain During Movement Preparation and Execution in Stroke Patients.
Front Neurosci. 2020 Aug 20;14:827. doi: 10.3389/fnins.2020.00827. eCollection 2020.

引用本文的文献

1
Novel Robotic Balloon-Based Device for Wrist-Extension Therapy of Hemiparesis Stroke Patients.
Sensors (Basel). 2025 Feb 23;25(5):1360. doi: 10.3390/s25051360.
2
A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface.
Front Neurosci. 2023 Jun 2;17:1086472. doi: 10.3389/fnins.2023.1086472. eCollection 2023.
3
Time-Varying Effective Connectivity for Describing the Dynamic Brain Networks of Post-stroke Rehabilitation.
Front Aging Neurosci. 2022 May 24;14:911513. doi: 10.3389/fnagi.2022.911513. eCollection 2022.

本文引用的文献

1
A survey of brain network analysis by electroencephalographic signals.
Cogn Neurodyn. 2022 Feb;16(1):17-41. doi: 10.1007/s11571-021-09689-8. Epub 2021 Jun 14.
2
Rehabilitation of motor function in children with cerebral palsy based on motor imagery.
Cogn Neurodyn. 2021 Dec;15(6):939-948. doi: 10.1007/s11571-021-09672-3. Epub 2021 Mar 14.
3
EEG Changes in Time and Time-Frequency Domain During Movement Preparation and Execution in Stroke Patients.
Front Neurosci. 2020 Aug 20;14:827. doi: 10.3389/fnins.2020.00827. eCollection 2020.
4
Enhanced Motor Imagery Based Brain- Computer Interface via FES and VR for Lower Limbs.
IEEE Trans Neural Syst Rehabil Eng. 2020 Aug;28(8):1846-1855. doi: 10.1109/TNSRE.2020.3001990. Epub 2020 Jun 12.
5
On the Nature of Explanations Offered by Network Science: A Perspective From and for Practicing Neuroscientists.
Top Cogn Sci. 2020 Oct;12(4):1272-1293. doi: 10.1111/tops.12504. Epub 2020 May 22.
6
Predicting individual decision-making responses based on single-trial EEG.
Neuroimage. 2020 Feb 1;206:116333. doi: 10.1016/j.neuroimage.2019.116333. Epub 2019 Nov 4.
8
Reconfiguration patterns of large-scale brain networks in motor imagery.
Brain Struct Funct. 2019 Mar;224(2):553-566. doi: 10.1007/s00429-018-1786-y. Epub 2018 Nov 12.
10
The Dynamic Brain Networks of Motor Imagery: Time-Varying Causality Analysis of Scalp EEG.
Int J Neural Syst. 2019 Feb;29(1):1850016. doi: 10.1142/S0129065718500168. Epub 2018 Apr 11.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验