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皮质运动区的神经团块建模及α节律变化机制

Neural Mass Modeling in the Cortical Motor Area and the Mechanism of Alpha Rhythm Changes.

作者信息

Zhang Yuanyuan, Li Zhaoying, Xu Hang, Song Ziang, Xie Ping, Wei Penghu, Zhao Guoguang

机构信息

Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100032, China.

Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China.

出版信息

Sensors (Basel). 2024 Dec 25;25(1):56. doi: 10.3390/s25010056.

Abstract

Investigating the physiological mechanisms in the motor cortex during rehabilitation exercises is crucial for assessing stroke patients' progress. This study developed a single-channel Jansen neural mass model to explore the relationship between model parameters and motor cortex mechanisms. Firstly, EEG signals were recorded from 11 healthy participants under 20%, 40%, and 60% maximum voluntary contraction, and alpha rhythm power spectral density characteristics were extracted using the Welch power spectrum method. Furthermore, a single-channel neural mass model was constructed to analyze the impact of parameter variations on the average power of simulated signals. Finally, model parameters were adjusted to achieve feature fitting between the simulated signals and the average power of the alpha rhythm. Results showed that alpha rhythm average power in the contralateral cortical regions increased with higher grip force levels. Similarly, the power of the simulated signals also increased with specific parameter (, , and ) increases, closely approximating the measured EEG signal changes. The findings suggest that increasing grip force activates more motor neurons in the motor cortex and raises their firing rate. Neural mass modeling provides a computational neuroscience approach to understanding the dynamic changes in alpha rhythms in the motor cortex under different grip force levels.

摘要

研究康复训练期间运动皮层的生理机制对于评估中风患者的恢复情况至关重要。本研究开发了一种单通道詹森神经团模型,以探索模型参数与运动皮层机制之间的关系。首先,从11名健康参与者身上记录了在最大自主收缩的20%、40%和60%情况下的脑电图信号,并使用韦尔奇功率谱方法提取了α节律功率谱密度特征。此外,构建了一个单通道神经团模型,以分析参数变化对模拟信号平均功率的影响。最后,调整模型参数以实现模拟信号与α节律平均功率之间的特征拟合。结果表明,对侧皮质区域的α节律平均功率随着握力水平的提高而增加。同样,模拟信号的功率也随着特定参数( 、 和 )的增加而增加,与测量的脑电图信号变化密切近似。研究结果表明,增加握力会激活运动皮层中更多的运动神经元并提高其放电率。神经团建模提供了一种计算神经科学方法,以了解不同握力水平下运动皮层中α节律的动态变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0a3/11722664/166b84dc7b48/sensors-25-00056-g001.jpg

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