Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
Universidad de San Andrés, Buenos Aires, Argentina.
Commun Biol. 2022 Jun 29;5(1):638. doi: 10.1038/s42003-022-03576-6.
Significant advances have been made by identifying the levels of synchrony of the underlying dynamics of a given brain state. This research has demonstrated that non-conscious dynamics tend to be more synchronous than in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that different brain states are underpinned by dissociable spatiotemporal dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep and disorders of consciousness after coma). The model-free approach was based on Kuramoto's turbulence framework using coupled oscillators. This was extended by a measure of the information cascade across spatial scales. Complementarily, the model-based approach used exhaustive in silico perturbations of whole-brain models fitted to these measures. This allowed studying of the information encoding capabilities in given brain states. Overall, this framework demonstrates that elements from turbulence theory provide excellent tools for describing and differentiating between brain states.
通过确定给定脑状态下潜在动力学的同步水平，已经取得了重大进展。这项研究表明，非意识动力学往往比意识状态更同步，而意识状态则更不同步。在这里，我们超越了这种二分法，证明了不同的大脑状态是由可分离的时空动力学支撑的。我们研究了来自不同脑状态（静息状态、冥想、深度睡眠和昏迷后意识障碍）的人类神经影像学数据。无模型方法基于 Kuramoto 的湍流框架，使用耦合振荡器。通过测量跨空间尺度的信息级联，对其进行了扩展。作为补充，基于模型的方法使用了针对这些测量值进行的全脑模型的详尽计算机内扰动。这使得可以研究给定脑状态中的信息编码能力。总的来说，这个框架表明，湍流理论中的元素为描述和区分大脑状态提供了极好的工具。
