Dagnino Paulina Clara, Escrichs Anira, López-González Ane, Gosseries Olivia, Annen Jitka, Sanz Perl Yonatan, Kringelbach Morten L, Laureys Steven, Deco Gustavo
Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.
PLoS Comput Biol. 2024 May 3;20(5):e1011350. doi: 10.1371/journal.pcbi.1011350. eCollection 2024 May.
A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.
神经科学中的一个基本挑战是准确界定脑状态，并预测如何以及在何处对大脑进行干预以促使其转变。在此，我们研究了昏迷后患有意识障碍（DoC）的患者（最低意识状态和无反应觉醒状态）以及健康对照者的静息态功能磁共振成像（fMRI）数据。我们应用了无模型和基于模型的方法来帮助阐明DoC患者潜在的脑机制。无模型方法使我们能够将DoC患者和健康对照者的脑状态表征为概率性亚稳态子状态（PMS）空间。每组的PMS由具有不同发生概率的一系列独特模式（即亚稳态子状态）定义。在基于模型的方法中，我们将每个DoC组的PMS调整为一个因果性全脑模型。这使我们能够通过应用离线计算机模拟探测来探索促进转变的最佳策略。此外，这种方法使我们能够根据局部扰动的全局效应及其对刺激的敏感性来评估其影响，这是一种基于模型的生物标志物，能更深入地理解DoC背后的机制。我们的结果表明，在一种同步方案中实现了转变，其中躯体运动网络、丘脑、楔前叶和脑岛是对扰动最敏感的区域。这促使我们进一步开展工作，以持续深入了解脑功能和意识障碍的治疗方法。
