Martínez-Molina Noelia, Sanz-Perl Yonatan, Escrichs Anira, Kringelbach Morten L, Deco Gustavo
Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom.
Front Neuroinform. 2024 Mar 25;18:1382372. doi: 10.3389/fninf.2024.1382372. eCollection 2024.
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 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.
创伤性脑损伤(TBI)是一种常见疾病,主要特征为认知功能持续受损,给全球范围内的护理人员和医疗系统带来了沉重负担。至关重要的是,严重程度分类主要基于临床评估,而这些评估缺乏特异性,对长期残疾的预测能力也很差。在本综述中,我们首先描述了在湍流动力学框架内我们的无模型和基于模型的方法,以及我们对于它们如何可能有助于为TBI提供新的神经影像学生物标志物的展望。此外,我们报告了我们最近一项研究的主要发现,该研究通过应用湍流动力学框架(无模型方法)和霍普夫全脑计算模型(基于模型的方法)并结合微扰,研究了中度至重度TBI(msTBI)患者在一年自发恢复期间的纵向变化。鉴于TBI后存在神经炎症反应和神经退行性变风险增加的情况,我们还提供了探索与基因组信息关联的未来方向。此外,根据我们最近的研究结果,我们讨论了全脑计算建模如何可能推进我们对msTBI后结构连接中断对全脑动力学影响的理解。最后,我们提出了全脑计算建模可能有助于确定促进TBI恢复的深部脑刺激最佳脑靶点的未来途径。