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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurol. | doi: 10.3389/fneur.2018.00676

Longitudinal Bedside Assessments of Brain Networks in Disorders of Consciousness: Case Reports from the Field

 Corinne Bareham1, 2,  Judith Allanson3,  Neil Roberts4, Peter J. Hutchinson1, 2, John D. Pickard1, 2, David K. Menon2 and  Srivas Chennu1, 2, 5*
  • 1Department of Clinical Neurosciences, University of Cambridge, United Kingdom
  • 2Division of Anaesthesia, University of Cambridge, United Kingdom
  • 3Cambridge University Hospitals NHS Foundation Trust, United Kingdom
  • 4Sawbridgeworth Medical Services, Jacobs & Gardens Neuro Centres, United Kingdom
  • 5School of Computing, University of Kent, United Kingdom

Clinicians are regularly faced with the difficult challenge of diagnosing consciousness after severe brain injury. As such, as many as 40% of minimally conscious patients who demonstrate fluctuations in arousal and awareness are known to be misdiagnosed as unresponsive/vegetative based on clinical consensus. Further, a significant minority of patients show evidence of hidden awareness not evident in their behaviour. Despite this, clinical assessments of behaviour are commonly used as bedside indicators of consciousness. Recent advances in functional high-density electroencephalography (hdEEG) have indicated that specific patterns of resting brain connectivity measured at the bedside are strongly correlated with the re-emergence of consciousness after brain injury. We report case studies of four patients with traumatic brain injury who underwent regular assessments of hdEEG connectivity and Coma Recovery Scale-Revised (CRS-R) at the bedside, as part of an ongoing longitudinal study. The first, a patient in an unresponsive wakefulness state (UWS), progressed to a minimally-conscious state several years after injury. HdEEG measures of alpha network centrality in this patient tracked this behavioural improvement. The second patient, contrasted with patient 1, presented with a persistent UWS diagnosis that paralleled with stability on the same alpha network centrality measure. Patient 3, diagnosed as minimally conscious minus (MCS-), demonstrated a significant late increase in behavioural awareness to minimally conscious plus (MCS+). This patient’s hdEEG connectivity across the previous 18 months showed a trajectory consistent with this increase alongside a decrease in delta power. Patient 4 contrasted with patient 3, with a persistent MCS- diagnosis that was similarly tracked by consistently high delta power over time. Across these contrasting cases, hdEEG connectivity captures both stability and recovery of behavioural trajectories both within and between patients. Our preliminary findings highlight the feasibility of bedside hdEEG assessments in the rehabilitation context and suggest that they can complement clinical evaluation with portable, accurate and timely generation of brain-based patient profiles. Further, such hdEEG assessments could be used to estimate the potential utility of complementary neuroimaging assessments, and to evaluate the efficacy of interventions.

Keywords: Consciousness, Electroencephalography, brain networks, Longitudinal assessment, Minimally Conscious State, Unresponsive wakefulness state, disorders of consciousness

Received: 08 Feb 2018; Accepted: 27 Jul 2018.

Edited by:

Olivia Gosseries, University of Liège, Belgium

Reviewed by:

Jacobo D. Sitt, Institut National de la Santé et de la Recherche Médicale (INSERM), France
Armand Mensen, Universität Bern, Switzerland  

Copyright: © 2018 Bareham, Allanson, Roberts, Hutchinson, Pickard, Menon and Chennu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Srivas Chennu, University of Kent, School of Computing, Canterbury, United Kingdom, sc785@kent.ac.uk