Welcome to the UPF Digital Repository

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

Show simple item record

dc.contributor.author Vohryzek, Jakub
dc.contributor.author Cabral, Joana
dc.contributor.author Castaldo, Francesca
dc.contributor.author Sanz-Perl, Yonatan
dc.contributor.author Lord, Louis-David
dc.contributor.author Fernandes, Henrique M.
dc.contributor.author Litvak, Vladimir
dc.contributor.author Kringelbach, Morten L.
dc.contributor.author Deco, Gustavo
dc.date.accessioned 2023-03-13T07:44:53Z
dc.date.available 2023-03-13T07:44:53Z
dc.date.issued 2023
dc.identifier.citation 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
dc.identifier.issn 2001-0370
dc.identifier.uri http://hdl.handle.net/10230/56181
dc.description.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.
dc.description.sponsorship The authors declare that they have no conflict of interest. J.V. is supported by the EU H2020 FET Proactive project Neurotwin grant agreement no. 101017716. J.C. is funded by the Portuguese Foundation for Science and Technology grants UIDB/50026/2020, UIDP/50026/2020, la Caixa” Foundation (LCF/BQ/PR22/11920014) and CEECIND/ 03325/2017, Portugal. F.C. is funded by the EU-project euSNN European School of Network Neuroscience (MSCA-ITN-ETN H2020-860563). The Wellcome Centre for Human Neuroimaging is supported by core funding from Wellcome [203147/Z/16/Z]. M.L.K. is supported by the Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117), and Centre for Eudaimonia and Human Flourishing at Linacre College funded by the Pettit and Carlsberg Foundations. G.D. is supported by the Spanish national research project (AEI-PID2019-105772GB I00/AEI/10.13039 /501100011033) funded by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Computational and Structural Biotechnology Journal. 2023;21:335-45
dc.rights © 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 (https://creativecommons.org/licenses/by/4.0/).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Dynamic sensitivity analysis: defining personalised strategies to drive brain state transitions via whole brain modelling
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.csbj.2022.11.060
dc.subject.keyword Spatio-temporal dynamics
dc.subject.keyword Brain stimulation
dc.subject.keyword Whole-brain models
dc.subject.keyword Brain State
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101017716
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/86056
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-105772GB-I00
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


This item appears in the following Collection(s)

Show simple item record

Search DSpace

Advanced Search


My Account


Compliant to Partaking