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

Authors :
Noelia Martínez-Molina
Yonatan Sanz-Perl
Anira Escrichs
Morten L. Kringelbach
Gustavo Deco
Source :
Frontiers in Neuroinformatics, Vol 18 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

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.

Details

Language :
English
ISSN :
16625196
Volume :
18
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neuroinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.00a5a37b81f243e19aeb495d92549966
Document Type :
article
Full Text :
https://doi.org/10.3389/fninf.2024.1382372