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The prognostic value of resting-state EEG in acute post-traumatic unresponsive states
O'Donnell, Alice Pauli, Ruth Banellis, Leah Sokoliuk, Rodika Hayton, Tom Sturman, Steve Yakoub, Kamal M Belli, Antonio Chennu, Srivas
O'Donnell, Alice
Pauli, Ruth
Banellis, Leah
Sokoliuk, Rodika
Hayton, Tom
Sturman, Steve
Yakoub, Kamal M
Belli, Antonio
Chennu, Srivas
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Epub Date
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2021-03-17
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Abstract
Accurate early prognostication is vital for appropriate long-term care decisions after traumatic brain injury. While measures of resting-state EEG oscillations and their network properties, derived from graph theory, have been shown to provide clinically useful information regarding diagnosis and recovery in patients with chronic disorders of consciousness, little is known about the value of these network measures when calculated from a standard clinical low-density EEG in the acute phase post-injury. To investigate this link, we first validated a set of measures of oscillatory network features between high-density and low-density resting-state EEG in healthy individuals, thus ensuring accurate estimation of underlying cortical function in clinical recordings from patients. Next, we investigated the relationship between these features and the clinical picture and outcome of a group of 18 patients in acute post-traumatic unresponsive states who were not following commands 2 days+ after sedation hold. While the complexity of the alpha network, as indexed by the standard deviation of the participation coefficients, was significantly related to the patients' clinical picture at the time of EEG, no network features were significantly related to outcome at 3 or 6 months post-injury. Rather, mean relative alpha power across all electrodes improved the accuracy of outcome prediction at 3 months relative to clinical features alone. These results highlight the link between the alpha rhythm and clinical signs of consciousness and suggest the potential for simple measures of resting-state EEG band power to provide a coarse snapshot of brain health for stratification of patients for rehabilitation, therapy and assessments of both covert and overt cognition.
Citation
Alice O’Donnell, Ruth Pauli, Leah Banellis, Rodika Sokoliuk, Tom Hayton, Steve Sturman, Tonny Veenith, Kamal M Yakoub, Antonio Belli, Srivas Chennu, Damian Cruse, The prognostic value of resting-state EEG in acute post-traumatic unresponsive states, Brain Communications, Volume 3, Issue 2, 2021, fcab017, https://doi.org/10.1093/braincomms/fcab017
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PubMed ID
33855295 (pubmed)
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Journal article
Language
en
Description
© 2021 The Authors. Published by Oxford University Press. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website:https://doi.org/10.1093/braincomms/fcab017
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ISSN
2632-1297
EISSN
2632-1297
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Sponsors
This study was funded by a New Investigator Research Grant from the UK’s Medical Research Council to Damian Cruse (reference: MR/P013228/1).
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Licence for published version: Creative Commons Attribution 4.0 International