Dynamic sensitivity analysis: defining personalised strategies to drive brain state transitions via whole brain modelling

Vohryzek, Jakub; Cabral, Joana; Castaldo, Francesca; Sanz-Perl, Yonatan; Lord, Louis-David; Fernandes, Henrique M.; Litvak, Vladimir; Kringelbach, Morten L.; Deco, Gustavo

doi:http://dx.doi.org/10.1016/j.csbj.2022.11.060

Dynamic sensitivity analysis: defining personalised strategies to drive brain state transitions via whole brain modelling

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Vohryzek J, Cabral J, Castaldo F, Sanz-Perl Y, Lord LD, Fernandes HM, et al. Dynamic sensitivity analysis: defining personalised strategies to drive brain state transitions via whole brain modelling. Computational and Structural Biotechnology Journal. 2023;21:335-45. DOI: 10.1016/j.csbj.2022.11.060

Abstract

Traditionally, in neuroimaging, model-free analyses are used to find significant differences between brain states via signal detection theory. Depending on the a priori assumptions about the underlying data, different spatio-temporal features can be analysed. Alternatively, model-based techniques infer features from the data and compare significance from model parameters. However, to assess transitions from one brain state to another remains a challenge in current paradigms. Here, we introduce a “Dynamic Sensitivity Analysis” framework that quantifies transitions between brain states in terms of stimulation ability to rebalance spatio-temporal brain activity towards a target state such as healthy brain dynamics. In practice, it means building a whole-brain model fitted to the spatio-temporal description of brain dynamics, and applying systematic stimulations in-silico to assess the optimal strategy to drive brain dynamics towards a target state. Further, we show how Dynamic Sensitivity Analysis extends to various brain stimulation paradigms, ultimately contributing to improving the efficacy of personalised clinical interventions.