Publications

Experimental and clinical studies of consciousness identify brain states (i.e. quasi-stable functional cerebral organization) in a non-systematic manner and largely independent of the research into brain state modulation. In this narrative review, we synthesize advances in the identification of brain states associated with consciousness in animal models and physiological (sleep), pharmacological (anaesthesia) and pathological (disorders of consciousness) states of altered consciousness in humans. We show that in reduced consciousness the frequencies in which the brain operates are slowed down and that the pattern of functional communication is sparser, less efficient, and less complex. The results also highlight damaged resting-state networks, in particular the default mode network, decreased connectivity in long-range connections and especially in the thalamocortical loops. Next, we show that therapeutic approaches to treat disorders of consciousness, through pharmacology (e.g. amantadine, zolpidem), and (non-) invasive brain stimulation (e.g. transcranial direct current stimulation, deep brain stimulation) have shown partial effectiveness in promoting consciousness recovery. Although some features of conscious brain states may improve in response to neuromodulation, targeting often remains non-specific and does not always lead to (behavioural) improvements. The fields of brain state identification and neuromodulation of brain states in relation to consciousness are showing fascinating developments that, when integrated, might propel the development of new and better-targeted techniques for disorders of consciousness. We here propose a therapeutic framework for the identification and modulation of brain states to facilitate the interaction between the two fields. We propose that brain states should be identified in a predictive setting, followed by theoretical and empirical testing (i.e. in animal models, under anaesthesia and in patients with a disorder of consciousness) of neuromodulation techniques to promote consciousness in line with such predictions. This framework further helps to identify where challenges and opportunities lay for the maturation of brain state research in the context of states of consciousness. It will become apparent that one angle of opportunity is provided through the addition of computational modelling. Finally, it aids in recognizing possibilities and obstacles for the clinical translation of these diagnostic techniques and neuromodulation treatment options across both the multimodal and multi-species approaches outlined throughout the review.

JTD Keywords: (disorders of) consciousness, Anaesthesia, Animal model, Animal models, Area induces reanimation, Brain states, Direct-current stimulation, Disorder, Electrical-stimulation, Functional connectivity, General-anesthesia, Neuromodulation, Propofol-induced loss, Thalamic-stimulation, Transcranial magnetic stimulation, Vegetative state

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. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions.

JTD Keywords: Consciousness in the Clinic, Computational neuroscience, Complexity measures, Clinical Neuroscience, Measures of consciousness

This chapter introduces the “Capabilities” section of the Handbook of Living Machines. Where the previous section considered building blocks, we recognize that components or modules do not automatically make systems. Hence, in the remainder of this handbook, the emphasis is toward the capabilities of living systems and their emulation in artifacts. Capabilities often arise from the integration of multiple components and thus sensitize us to the need to develop a system-level perspective on living machines. Here we summarize and consider the 14 contributions in this section which cover perception, action, cognition, communication, and emotion, and the integration of these through cognitive architectures into systems that can emulate the full gamut of integrated behaviors seen in animals including, potentially, our own capacity for consciousness.

JTD Keywords: Action, Cognition, Cognitive architecture, Communication, Consciousness, Emotion, Perception