Stikvoort Wiep, Pérez-Ordoyo Eider, Mindlin Iván, Escrichs Anira, Sitt Jacobo D, Kringelbach Morten L, Deco Gustavo, Perl Yonatan Sanz
Center for Brain and Cognition, Department of Information Technologies and Communications (DTIC), Universitat Pompeu Fabra, Barcelona, Spain.
Paris Brain Institute, ICM, Inserm, CNRS, Sorbonne Université, Paris, France.
PLoS Comput Biol. 2025 Jun 6;21(6):e1013150. doi: 10.1371/journal.pcbi.1013150. eCollection 2025 Jun.
Assessing someone's level of consciousness is a complex matter, and attempts have been made to aid clinicians in these assessments through metrics based on neuroimaging data. Many studies have empirically investigated measures related to the complexity elicited after the brain is stimulated to quantify the level of consciousness across different states. Here we hypothesized that the level of non-equilibrium dynamics of the unperturbed brain already contains the information needed to know how the system will react to an external stimulus. We created personalized whole-brain models fitted to resting state fMRI data recorded in participants in altered states of consciousness (e.g., deep sleep, disorders of consciousness) to infer the effective connections underlying their brain dynamics. We then measured the out-of-equilibrium nature of the unperturbed brain by evaluating the level of asymmetry of the inferred connectivity, the time irreversibility in each model and compared this with the elicited complexity generated after in silico perturbations, using a simulated fMRI-based version of the Perturbational Complexity Index, a measure that has been shown to distinguish different levels of consciousness in in vivo settings. Crucially, we found that states of consciousness involving lower arousal and/or lower awareness had a lower level of asymmetry in their effective connectivities, a lower level of irreversibility in their simulated dynamics, and a lower complexity compared to control subjects. We show that the asymmetry in the underlying connections drives the nonequilibrium state of the system and in turn the differences in complexity as a response to the external stimuli.
评估某人的意识水平是一件复杂的事情，人们已经尝试通过基于神经影像数据的指标来帮助临床医生进行这些评估。许多研究已经通过实证研究了与大脑受到刺激后引发的复杂性相关的测量方法，以量化不同状态下的意识水平。在这里，我们假设未受干扰的大脑的非平衡动力学水平已经包含了了解系统将如何对外界刺激做出反应所需的信息。我们创建了个性化的全脑模型，使其与处于意识改变状态（如深度睡眠、意识障碍）的参与者记录的静息态功能磁共振成像数据相匹配，以推断其大脑动力学背后的有效连接。然后，我们通过评估推断连接性的不对称程度、每个模型中的时间不可逆性来测量未受干扰的大脑的非平衡性质，并将其与基于计算机模拟微扰后产生的引发复杂性进行比较，使用基于功能磁共振成像模拟版本的微扰复杂性指数，该指标已被证明能够在体内环境中区分不同的意识水平。至关重要的是，我们发现与对照组相比，涉及较低觉醒和/或较低意识的意识状态在其有效连接性方面具有较低的不对称水平，在其模拟动力学方面具有较低的不可逆性水平，并且复杂性较低。我们表明，潜在连接中的不对称驱动了系统的非平衡状态，进而导致了作为对外界刺激反应的复杂性差异。
