Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury

Autores
Martínez Molina, Noelia; Sanz Perl Hernandez, Yonatan; Escrichs, Anira; Kringelbach, Morten L.; Deco, Gustavo
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Traumatic Brain Injury (TBI) is a prevalent disorder mostly characterized by persistent impairments in cognitive function that poses a substantial burden on caregivers and the healthcare system worldwide. Crucially, severity classification is primarily based on clinical evaluations, which are non-specific and poorly predictive of long-term disability. In this Mini Review, we first provide a description of our model-free and model-based approaches within the turbulent dynamics framework as well as our vision on how they can potentially contribute to provide new neuroimaging biomarkers for TBI. In addition, we report the main findings of our recent study examining longitudinal changes in moderate-severe TBI (msTBI) patients during a one year spontaneous recovery by applying the turbulent dynamics framework (model-free approach) and the Hopf whole-brain computational model (model-based approach) combined with in silico perturbations. Given the neuroinflammatory response and heightened risk for neurodegeneration after TBI, we also offer future directions to explore the association with genomic information. Moreover, we discuss how whole-brain computational modeling may advance our understanding of the impact of structural disconnection on whole-brain dynamics after msTBI in light of our recent findings. Lastly, we suggest future avenues whereby whole-brain computational modeling may assist the identification of optimal brain targets for deep brain stimulation to promote TBI recovery.
Fil: Martínez Molina, Noelia. Universitat Pompeu Fabra; España
Fil: Sanz Perl Hernandez, Yonatan. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Escrichs, Anira. Universitat Pompeu Fabra; España
Fil: Kringelbach, Morten L.. University of Oxford; Reino Unido
Fil: Deco, Gustavo. Universitat Pompeu Fabra; España
Materia
Neuroimaging
Turbulence
Traumatic Brain Injury
Whole-brain model
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/251825
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injuryMartínez Molina, NoeliaSanz Perl Hernandez, YonatanEscrichs, AniraKringelbach, Morten L.Deco, GustavoNeuroimagingTurbulenceTraumatic Brain InjuryWhole-brain modelhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Traumatic Brain Injury (TBI) is a prevalent disorder mostly characterized by persistent impairments in cognitive function that poses a substantial burden on caregivers and the healthcare system worldwide. Crucially, severity classification is primarily based on clinical evaluations, which are non-specific and poorly predictive of long-term disability. In this Mini Review, we first provide a description of our model-free and model-based approaches within the turbulent dynamics framework as well as our vision on how they can potentially contribute to provide new neuroimaging biomarkers for TBI. In addition, we report the main findings of our recent study examining longitudinal changes in moderate-severe TBI (msTBI) patients during a one year spontaneous recovery by applying the turbulent dynamics framework (model-free approach) and the Hopf whole-brain computational model (model-based approach) combined with in silico perturbations. Given the neuroinflammatory response and heightened risk for neurodegeneration after TBI, we also offer future directions to explore the association with genomic information. Moreover, we discuss how whole-brain computational modeling may advance our understanding of the impact of structural disconnection on whole-brain dynamics after msTBI in light of our recent findings. Lastly, we suggest future avenues whereby whole-brain computational modeling may assist the identification of optimal brain targets for deep brain stimulation to promote TBI recovery.Fil: Martínez Molina, Noelia. Universitat Pompeu Fabra; EspañaFil: Sanz Perl Hernandez, Yonatan. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Escrichs, Anira. Universitat Pompeu Fabra; EspañaFil: Kringelbach, Morten L.. University of Oxford; Reino UnidoFil: Deco, Gustavo. Universitat Pompeu Fabra; EspañaFrontiers Media2024-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/251825Martínez Molina, Noelia; Sanz Perl Hernandez, Yonatan; Escrichs, Anira; Kringelbach, Morten L.; Deco, Gustavo; Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury; Frontiers Media; Frontiers in Neuroinformatics; 18; 3-2024; 1-71662-5196CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fninf.2024.1382372/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fninf.2024.1382372info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:45:15Zoai:ri.conicet.gov.ar:11336/251825instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:45:15.758CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury
title Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury
spellingShingle Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury
Martínez Molina, Noelia
Neuroimaging
Turbulence
Traumatic Brain Injury
Whole-brain model
title_short Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury
title_full Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury
title_fullStr Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury
title_full_unstemmed Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury
title_sort Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury
dc.creator.none.fl_str_mv Martínez Molina, Noelia
Sanz Perl Hernandez, Yonatan
Escrichs, Anira
Kringelbach, Morten L.
Deco, Gustavo
author Martínez Molina, Noelia
author_facet Martínez Molina, Noelia
Sanz Perl Hernandez, Yonatan
Escrichs, Anira
Kringelbach, Morten L.
Deco, Gustavo
author_role author
author2 Sanz Perl Hernandez, Yonatan
Escrichs, Anira
Kringelbach, Morten L.
Deco, Gustavo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Neuroimaging
Turbulence
Traumatic Brain Injury
Whole-brain model
topic Neuroimaging
Turbulence
Traumatic Brain Injury
Whole-brain model
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Traumatic Brain Injury (TBI) is a prevalent disorder mostly characterized by persistent impairments in cognitive function that poses a substantial burden on caregivers and the healthcare system worldwide. Crucially, severity classification is primarily based on clinical evaluations, which are non-specific and poorly predictive of long-term disability. In this Mini Review, we first provide a description of our model-free and model-based approaches within the turbulent dynamics framework as well as our vision on how they can potentially contribute to provide new neuroimaging biomarkers for TBI. In addition, we report the main findings of our recent study examining longitudinal changes in moderate-severe TBI (msTBI) patients during a one year spontaneous recovery by applying the turbulent dynamics framework (model-free approach) and the Hopf whole-brain computational model (model-based approach) combined with in silico perturbations. Given the neuroinflammatory response and heightened risk for neurodegeneration after TBI, we also offer future directions to explore the association with genomic information. Moreover, we discuss how whole-brain computational modeling may advance our understanding of the impact of structural disconnection on whole-brain dynamics after msTBI in light of our recent findings. Lastly, we suggest future avenues whereby whole-brain computational modeling may assist the identification of optimal brain targets for deep brain stimulation to promote TBI recovery.
Fil: Martínez Molina, Noelia. Universitat Pompeu Fabra; España
Fil: Sanz Perl Hernandez, Yonatan. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Escrichs, Anira. Universitat Pompeu Fabra; España
Fil: Kringelbach, Morten L.. University of Oxford; Reino Unido
Fil: Deco, Gustavo. Universitat Pompeu Fabra; España
description Traumatic Brain Injury (TBI) is a prevalent disorder mostly characterized by persistent impairments in cognitive function that poses a substantial burden on caregivers and the healthcare system worldwide. Crucially, severity classification is primarily based on clinical evaluations, which are non-specific and poorly predictive of long-term disability. In this Mini Review, we first provide a description of our model-free and model-based approaches within the turbulent dynamics framework as well as our vision on how they can potentially contribute to provide new neuroimaging biomarkers for TBI. In addition, we report the main findings of our recent study examining longitudinal changes in moderate-severe TBI (msTBI) patients during a one year spontaneous recovery by applying the turbulent dynamics framework (model-free approach) and the Hopf whole-brain computational model (model-based approach) combined with in silico perturbations. Given the neuroinflammatory response and heightened risk for neurodegeneration after TBI, we also offer future directions to explore the association with genomic information. Moreover, we discuss how whole-brain computational modeling may advance our understanding of the impact of structural disconnection on whole-brain dynamics after msTBI in light of our recent findings. Lastly, we suggest future avenues whereby whole-brain computational modeling may assist the identification of optimal brain targets for deep brain stimulation to promote TBI recovery.
publishDate 2024
dc.date.none.fl_str_mv 2024-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/251825
Martínez Molina, Noelia; Sanz Perl Hernandez, Yonatan; Escrichs, Anira; Kringelbach, Morten L.; Deco, Gustavo; Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury; Frontiers Media; Frontiers in Neuroinformatics; 18; 3-2024; 1-7
1662-5196
CONICET Digital
CONICET
url http://hdl.handle.net/11336/251825
identifier_str_mv Martínez Molina, Noelia; Sanz Perl Hernandez, Yonatan; Escrichs, Anira; Kringelbach, Morten L.; Deco, Gustavo; Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury; Frontiers Media; Frontiers in Neuroinformatics; 18; 3-2024; 1-7
1662-5196
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fninf.2024.1382372/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/fninf.2024.1382372
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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