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

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• dc.contributor.author Kurtin, Danielle L.
• dc.contributor.author Giunchiglia, Valentina
• dc.contributor.author Vohryzek, Jakub
• dc.contributor.author Cabral, Joana
• dc.contributor.author Skeldon, Anne C.
• dc.contributor.author Violante, Ines R.
• dc.date.accessioned 2023-07-07T07:01:24Z
• dc.date.available 2023-07-07T07:01:24Z
• dc.date.issued 2023
• dc.description.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.
• dc.format.mimetype application/pdf
• dc.identifier.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
• dc.identifier.doi http://dx.doi.org/10.1016/j.neuroimage.2023.120042
• dc.identifier.issn 1053-8119
• dc.identifier.uri http://hdl.handle.net/10230/57504
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof NeuroImage. 2023;275:120042.
• dc.rights © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword phenomenological models
• dc.subject.keyword biophysical models
• dc.subject.keyword oscillatory models
• dc.subject.keyword brain stimulation
• dc.subject.keyword model construction
• dc.subject.keyword parameter optimisation
• dc.title Moving from phenomenological to predictive modelling: progress and pitfalls of modelling brain stimulation in-silico
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion