Institute of Mathematics, University of Rostock, Rostock, Germany.
Laboratory of Mathematics and Informatics (ISCE), Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece.
Sci Rep. 2024 Aug 14;14(1):18919. doi: 10.1038/s41598-024-69456-7.
A large-scale biophysical network model for the isolated striatal body is developed to optimise potential intrastriatal deep brain stimulation applied to, e.g. obsessive-compulsive disorder. The model is based on modified Hodgkin-Huxley equations with small-world connectivity, while the spatial information about the positions of the neurons is taken from a detailed human atlas. The model produces neuronal spatiotemporal activity patterns segregating healthy from pathological conditions. Three biomarkers were used for the optimisation of stimulation protocols regarding stimulation frequency, amplitude and localisation: the mean activity of the entire network, the frequency spectrum of the entire network (rhythmicity) and a combination of the above two. By minimising the deviation of the aforementioned biomarkers from the normal state, we compute the optimal deep brain stimulation parameters, regarding position, amplitude and frequency. Our results suggest that in the DBS optimisation process, there is a clear trade-off between frequency synchronisation and overall network activity, which has also been observed during in vivo studies.
我们开发了一个用于孤立纹状体的大规模生物物理网络模型，以优化潜在的用于治疗强迫症等疾病的纹状体内深部脑刺激。该模型基于带有小世界连接的修正 Hodgkin-Huxley 方程，而神经元位置的空间信息则来自详细的人类图谱。该模型产生了区分健康和病理条件的神经元时空活动模式。我们使用了三个生物标志物来优化刺激方案，包括刺激频率、幅度和定位：整个网络的平均活动、整个网络的频谱（节律性）以及上述两者的组合。通过最小化上述生物标志物与正常状态的偏差，我们计算出关于位置、幅度和频率的最佳深部脑刺激参数。我们的结果表明，在 DBS 优化过程中，频率同步和整体网络活动之间存在明显的权衡，这在体内研究中也观察到了。
