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Bedside EEG predicts longitudinal behavioural changes in disorders of consciousness

Bareham, Corinne A., Roberts, Neil, Allanson, Judith, Hutchinson, Peter J.A., Pickard, John D., Menon, David K., Chennu, Srivas (2020) Bedside EEG predicts longitudinal behavioural changes in disorders of consciousness. NeuroImage: Clinical, 28 . Article Number 102372. E-ISSN 2213-1582. (doi:10.1016/j.nicl.2020.102372) (KAR id:82407)

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Abstract

Providing an accurate prognosis for prolonged disorder of consciousness (pDOC) patients remains a clinical challenge. Large cross-sectional studies have demonstrated the diagnostic and prognostic value of functional brain networks measured using high-density electroencephalography (hdEEG). Nonetheless, the prognostic value of these neural measures has yet to be assessed by longitudinal follow-up. We address this gap by assessing the utility of hdEEG to prognosticate long-term behavioural outcome, employing longitudinal data collected from a cohort of patients assessed systematically with resting hdEEG and the Coma Recovery Scale-Revised (CRS-R) at the bedside over a period of two years. We used canonical correlation analysis to relate clinical (including CRS-R scores combined with demographic variables) and hdEEG variables to each other. This analysis revealed that the patient’s age, and the hdEEG theta band power and alpha band connectivity, contributed most significantly to the relationship between hdEEG and clinical variables. Further, we found that hdEEG measures recorded at the time of assessment augmented clinical measures in predicting CRS-R scores at the next assessment. Moreover, the rate of hdEEG change not only predicted later changes in CRS-R scores, but also outperformed clinical measures in terms of prognostic power. Together, these findings suggest that improvements in functional brain networks precede changes in behavioural awareness in pDOC. We demonstrate here that bedside hdEEG assessments conducted at specialist nursing homes are feasible, have clinical utility, and can complement clinical knowledge and systematic behavioural assessments to inform prognosis and care.

Item Type: Article
DOI/Identification number: 10.1016/j.nicl.2020.102372
Subjects: Q Science
Divisions: Faculties > Sciences > School of Computing
Depositing User: Srivas Chennu
Date Deposited: 10 Aug 2020 21:35 UTC
Last Modified: 06 Oct 2020 09:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/82407 (The current URI for this page, for reference purposes)
Chennu, Srivas: https://orcid.org/0000-0002-6840-2941
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