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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; ... Deco, Gustavo; + view all (2023) Dynamic sensitivity analysis: Defining personalised strategies to drive brain state transitions via whole brain modelling. Computational and Structural Biotechnology Journal , 21 pp. 335-345. 10.1016/j.csbj.2022.11.060. Green open access

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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.

Type: Article
Title: Dynamic sensitivity analysis: Defining personalised strategies to drive brain state transitions via whole brain modelling
Location: Netherlands
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.csbj.2022.11.060
Publisher version: https://doi.org/10.1016/j.csbj.2022.11.060
Language: English
Additional information: © 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).
Keywords: Spatio-temporal dynamics, Brain stimulation, Whole-brain models, Brain State
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10164155
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