Arsiwalla Xerxes D, Verschure Paul
Institute for Bioengineering of Catalunya, Barcelona, Spain.
Barcelona Institute for Science and Technology, Barcelona, Spain.
Front Neurosci. 2018 Jun 27;12:424. doi: 10.3389/fnins.2018.00424. eCollection 2018.
The grand quest for a scientific understanding of consciousness has given rise to many new theoretical and empirical paradigms for investigating the phenomenology of consciousness as well as clinical disorders associated to it. A major challenge in this field is to formalize computational measures that can reliably quantify global brain states from data. In particular, information-theoretic complexity measures such as integrated information have been proposed as measures of conscious awareness. This suggests a new framework to quantitatively classify states of consciousness. However, it has proven increasingly difficult to apply these complexity measures to realistic brain networks. In part, this is due to high computational costs incurred when implementing these measures on realistically large network dimensions. Nonetheless, complexity measures for quantifying states of consciousness are important for assisting clinical diagnosis and therapy. This article is meant to serve as a lookup table of measures of consciousness, with particular emphasis on clinical applicability. We consider both, principle-based complexity measures as well as empirical measures tested on patients. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions.
对意识进行科学理解的宏大探索催生了许多新的理论和实证范式，用于研究意识现象学以及与之相关的临床疾病。该领域的一个主要挑战是形式化能够可靠地从数据中量化全脑状态的计算方法。特别是，诸如整合信息等信息论复杂性度量已被提议作为意识觉知的度量。这暗示了一个用于定量分类意识状态的新框架。然而，已证明将这些复杂性度量应用于现实的脑网络越来越困难。部分原因在于在现实的大网络维度上实施这些度量时会产生高昂的计算成本。尽管如此，用于量化意识状态的复杂性度量对于辅助临床诊断和治疗很重要。本文旨在作为意识度量的查找表，特别强调临床适用性。我们既考虑基于原理的复杂性度量，也考虑在患者身上测试的实证度量。我们探讨这些度量在现实脑网络方面面临的挑战，并在必要时提出可能的解决方案。
