Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Leverhulme Centre for the Future of Intelligence, Cambridge, UK; The Alan Turing Institute, London, UK.
Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK.
Neuroimage. 2023 Apr 1;269:119926. doi: 10.1016/j.neuroimage.2023.119926. Epub 2023 Feb 3.
High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.
高水平的大脑功能被广泛认为是多个神经网络协调活动的结果。然而,由于缺乏对实验数据涌现的正式定义和实际量化,神经科学家一直无法通过经验检验这一长期存在的假说。在这里,我们通过利用最近提出的“综合信息分解”框架来研究这个基本问题,该框架为动态系统中的涌现提供了一种原则性的信息论方法,包括人类大脑。通过分析功能磁共振成像数据,我们的结果表明,在患有严重脑损伤的慢性无反应患者中,神经动力学的涌现和分层特征明显减弱。在功能水平上,我们证明涌现能力与大脑活动中分层组织的程度呈正相关。此外,通过结合网络控制理论和全脑生物物理建模的计算方法,我们表明,严重脑损伤患者涌现和分层动力学能力的降低可以通过患者结构连接组的破坏来从机制上解释。总的来说,我们的研究结果表明,严重脑损伤导致的慢性无反应可能与支持涌现所需的基本神经结构的结构性损伤有关。