The evolution of whole-brain turbulent dynamics during recovery from traumatic brain injury

IF 3.6 3区 医学 Q2 NEUROSCIENCES Network Neuroscience Pub Date : 2023-11-01 DOI:10.1162/netn_a_00346
Noelia Martínez-Molina, Anira Escrichs, Yonatan Sanz-Perl, Aleksi J. Sihvonen, Teppo Särkämö, Morten L. Kringelbach, Gustavo Deco
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Abstract

Abstract It has been previously shown that traumatic brain injury (TBI) is associated with reductions in metastability in large-scale networks in resting state fMRI (rsfMRI). However, little is known about how TBI affects the local level of synchronization and how this evolves during the recovery trajectory. Here, we applied a novel turbulent dynamics framework to investigate whole-brain dynamics using a rsfMRI dataset from a cohort of moderate-to-severe TBI patients and healthy controls (HCs). We first examined how several measures related to turbulent dynamics differ between HCs and TBI patients at 3-, 6- and 12-months post-injury. We found a significant reduction in these empirical measures after TBI, with the largest change at 6-months post-injury. Next, we built a Hopf whole-brain model with coupled oscillators and conducted in silico perturbations to investigate the mechanistic principles underlying the reduced turbulent dynamics found in the empirical data. A simulated attack was used to account for the effect of focal lesions. This revealed a shift to lower coupling parameters in the TBI dataset and, critically, decreased susceptibility and information encoding capability. These findings confirm the potential of the turbulent framework to characterize longitudinal changes in whole-brain dynamics and in the reactivity to external perturbations after TBI.
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创伤性脑损伤恢复期全脑湍流动力学的演变
先前已有研究表明,创伤性脑损伤(TBI)与静息状态功能磁共振成像(rsfMRI)中大规模网络亚稳态的降低有关。然而,对于TBI如何影响本地同步以及在恢复轨迹中如何演变,人们知之甚少。在这里,我们应用了一个新的湍流动力学框架,利用来自中重度TBI患者和健康对照(hc)的rsfMRI数据集来研究全脑动力学。我们首先研究了hc和TBI患者在损伤后3、6和12个月的湍流动力学相关的几种测量方法的差异。我们发现TBI后这些经验指标显著降低,损伤后6个月变化最大。接下来,我们建立了一个带有耦合振荡器的Hopf全脑模型,并进行了硅微扰来研究在经验数据中发现的减少湍流动力学的机制原理。模拟攻击被用来解释局灶性病变的影响。这揭示了TBI数据集中低耦合参数的转变,关键是,敏感性和信息编码能力下降。这些发现证实了湍流框架在描述脑外伤后全脑动力学的纵向变化和对外部扰动的反应性方面的潜力。
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来源期刊
Network Neuroscience
Network Neuroscience NEUROSCIENCES-
CiteScore
6.40
自引率
6.40%
发文量
68
审稿时长
16 weeks
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