Shao Hongyuan, Gu Guanghua, Guo Xiaonan, Li Xiaoli, Cui Dong
Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China; School of Information Science and Engineering, Yanshan University, Qinhuangdao, China.
Guangdong Artificial Intelligence and Digital Economy Laboratory, Guangzhou, China; School of Automation Science and Engineering, South China University of Technology, Guangzhou, China.
Comput Methods Programs Biomed. 2025 May;263:108675. doi: 10.1016/j.cmpb.2025.108675. Epub 2025 Feb 15.
Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuroregulation technique that influences brain dynamics, widely used to enhance cognitive abilities, treat neurological disorders, and aid rehabilitation. With the advancement of computational neuroscience, dynamic modeling analysis has become an important tool for understanding the mechanisms of tDCS.
In this study, we constructed a resting-state whole-brain model, similar to the human brain. By simulating tDCS, we analyzed its effects at different intensities on the whole-brain model. We used various electrophysiological measures to assess the impact of tDCS on brain functional networks and electrophysiological characteristics. In addition, we analyzed the network structures influenced by different tDCS intensities using graph theory measures and the small-world index. Finally, we analyzed the factors that could influence the observed phenomena.
The results indicate that within a certain range, tDCS can enhance the synchronicity of brain functional networks; however, excessive intensity results in a significant reduction in the benefits. We observed that electrical stimulation induces complex electrophysiological activities across widespread brain regions through network propagation. Networks influenced by low tDCS intensity achieve optimal states in graph theory metrics. Conversely, high tDCS intensity damages network structures, reducing information transmission efficiency. Finally, we found that these phenomena are closely related to the unique physiological structure of the human brain.
This study demonstrates a nonlinear dose-response relationship, revealing that network synchrony achieves optimal states only at appropriate tDCS intensities. This research provides theoretical support for the clinical application of tDCS and scientific guidance for selecting the most effective stimulation protocols.
经颅直流电刺激(tDCS)是一种影响脑动力学的非侵入性神经调节技术,广泛用于提高认知能力、治疗神经疾病和辅助康复。随着计算神经科学的发展,动态建模分析已成为理解tDCS机制的重要工具。
在本研究中,我们构建了一个类似于人类大脑的静息态全脑模型。通过模拟tDCS,我们分析了其在不同强度下对全脑模型的影响。我们使用各种电生理测量方法来评估tDCS对脑功能网络和电生理特征的影响。此外,我们使用图论测量和小世界指数分析了受不同tDCS强度影响的网络结构。最后,我们分析了可能影响观察到的现象的因素。
结果表明,在一定范围内,tDCS可以增强脑功能网络的同步性;然而,强度过大则会导致益处显著减少。我们观察到电刺激通过网络传播在广泛的脑区诱导复杂的电生理活动。受低tDCS强度影响的网络在图论指标中达到最佳状态。相反,高tDCS强度会破坏网络结构,降低信息传输效率。最后,我们发现这些现象与人类大脑独特的生理结构密切相关。
本研究证明了一种非线性剂量反应关系,表明只有在适当的tDCS强度下网络同步才能达到最佳状态。本研究为tDCS的临床应用提供了理论支持,并为选择最有效的刺激方案提供了科学指导。