Neuromodulation Laboratory, School of Psychology, University of Surrey, Guildford, GU2 7XH, United Kingdom; Department of Brain Sciences, Imperial College London, London, United Kingdom.
Department of Brain Sciences, Imperial College London, London, United Kingdom.
Neuroimage. 2023 May 15;272:120042. doi: 10.1016/j.neuroimage.2023.120042. Epub 2023 Mar 23.
Brain stimulation is an increasingly popular neuromodulatory tool used in both clinical and research settings; however, the effects of brain stimulation, particularly those of non-invasive stimulation, are variable. This variability can be partially explained by an incomplete mechanistic understanding, coupled with a combinatorial explosion of possible stimulation parameters. Computational models constitute a useful tool to explore the vast sea of stimulation parameters and characterise their effects on brain activity. Yet the utility of modelling stimulation in-silico relies on its biophysical relevance, which needs to account for the dynamics of large and diverse neural populations and how underlying networks shape those collective dynamics. The large number of parameters to consider when constructing a model is no less than those needed to consider when planning empirical studies. This piece is centred on the application of phenomenological and biophysical models in non-invasive brain stimulation. We first introduce common forms of brain stimulation and computational models, and provide typical construction choices made when building phenomenological and biophysical models. Through the lens of four case studies, we provide an account of the questions these models can address, commonalities, and limitations across studies. We conclude by proposing future directions to fully realise the potential of computational models of brain stimulation for the design of personalized, efficient, and effective stimulation strategies.
脑刺激是一种在临床和研究环境中越来越受欢迎的神经调节工具；然而，脑刺激的效果，特别是非侵入性刺激的效果，是可变的。这种可变性部分可以通过不完全的机制理解来解释，再加上可能的刺激参数的组合爆炸。计算模型是探索刺激参数海洋并描述其对大脑活动影响的有用工具。然而，在模拟刺激方面，建模的实用性依赖于其生物物理相关性，这需要考虑到大量不同的神经元群体的动力学，以及底层网络如何塑造这些集体动力学。在构建模型时需要考虑的参数数量与规划经验研究时需要考虑的参数数量一样多。这篇文章主要集中在非侵入性脑刺激的现象学和生物物理模型的应用上。我们首先介绍常见的脑刺激形式和计算模型，并提供构建现象学和生物物理模型时的典型构建选择。通过四个案例研究的视角，我们描述了这些模型可以解决的问题、跨研究的共性和局限性。最后，我们提出了未来的方向，以充分实现脑刺激计算模型在设计个性化、高效和有效的刺激策略方面的潜力。
