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.
Highlights
And efficient stratification of patients after a traumatic brain injury (TBI) requires accurate prognostication in the intensive care unit. Of those individuals who enter a coma as a result of a TBI, 40% will die in the intensive care unit, 40% will achieve a good recovery and 20% will develop a prolonged disorder of consciousness such as unresponsive wakefulness syndrome in which they appear entirely unaware of themselves and their environments.[1,2]
We can be confident that these five features, which have previously been linked to diagnosis and prognosis in high-density data from chronic disorders of consciousness, can be reliably estimated from the low-density montages typical of clinical EEG recordings
We tested the hypothesis that similar network measures in clinically-standard resting-state EEG recordings have prognostic value for acute posttraumatic unresponsive states
Summary
And efficient stratification of patients after a traumatic brain injury (TBI) requires accurate prognostication in the intensive care unit. Claassen et al.[3] demonstrated that a significant minority of patients (16/104) in the acute period after severe brain injury could modulate their EEG-detected brain activity in response to verbal instructions Half of those patients (eight in total) progressed to being able to function independently (i.e. Extended Glasgow Outcome Score (GOSE) !4),[4] compared with a quarter of patients who did not exhibit evidence of instructioninduced EEG modulations. This result builds on evidence of the diagnostic and prognostic utility of task-based EEG modulations in more chronic stages of brain injury.[5,6] These task-based approaches identify those patients with the highest level of residual (though covert) cognition and consciousness, allowing strong conclusions regarding long-term outcomes. While the active motor imagery approach allows for strong predictions about future recovery in the 15% of patients who return positive results (i.e. 16/104; see also8), there is little clinical benefit for the 85% of patients who return null results
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