Measuring the Complexity of Consciousness

TL;DR

This paper reviews and evaluates various complexity measures of consciousness, focusing on their theoretical basis, empirical testing, and clinical applicability, while addressing computational challenges in realistic brain network analysis.

Contribution

It provides a comprehensive overview of consciousness complexity measures, highlighting their potential for clinical use and proposing solutions to computational difficulties.

Findings

Complexity measures can help classify states of consciousness.

High computational costs limit application to large brain networks.

Empirical measures show promise in clinical settings.

Abstract

The quest for a scientific description of consciousness has given rise to new theoretical and empirical paradigms for the investigation of phenomenological contents as well as clinical disorders of consciousness. An outstanding challenge in the field is to develop measures that uniquely quantify global brain states tied to consciousness. In particular, information-theoretic complexity measures such as integrated information have recently 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 of these measures. 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.