Moving from phenomenological to predictive modelling: progress and pitfalls of modelling brain stimulation in-silico

Kurtin, Danielle L.; Giunchiglia, Valentina; Vohryzek, Jakub; Cabral, Joana; Skeldon, Anne C.; Violante, Ines R.

doi:http://dx.doi.org/10.1016/j.neuroimage.2023.120042

Moving from phenomenological to predictive modelling: progress and pitfalls of modelling brain stimulation in-silico

License

Citation

Kurtin DL, Giunchiglia V, Vohryzek J, Cabral J, Skeldon AC, Violante IR. Moving from phenomenological to predictive modelling: progress and pitfalls of modelling brain stimulation in-silico. Neuroimage. 2023;275:120042. DOI: 10.1016/j.neuroimage.2023.120042

Abstract

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.