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PLoS Comput Biol. 2014 Oct 16;10(10):e1003887. doi: 10.1371/journal.pcbi.1003887. eCollection 2014 Oct.

Spectral signatures of reorganised brain networks in disorders of consciousness.

Author information

1
Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom; Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, United Kingdom.
2
Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom.
3
Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom.
4
Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom.
5
Department of Psychology, University of California at Los Angeles, Los Angeles, California, United States of America.
6
Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, United Kingdom.
7
Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, United Kingdom; Laboratory of Cognitive and Social Neuroscience, Universidad Diego Portales, Santiago, Chile.
8
Laboratory of Cognitive and Social Neuroscience, Universidad Diego Portales, Santiago, Chile.
9
Division of Anaesthesia, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom.
10
The Brain and Mind Institute, Natural Sciences Centre, The University of Western Ontario, London, Ontario, Canada.
11
Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, United Kingdom; Department of Psychology, University of Cambridge, Cambridge, United Kingdom.

Abstract

Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.

PMID:
25329398
PMCID:
PMC4199497
DOI:
10.1371/journal.pcbi.1003887
[Indexed for MEDLINE]
Free PMC Article

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