The prognostic value of resting-state EEG in acute post-traumatic unresponsive states

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.

Keywords: EEG; alpha; coma; prognosis; traumatic brain injury.

Graphical Abstract

Figure 1

Relationships between EEG features and clinical picture. (A) Correlation between the first pair of canonical variates, including least squares line fit. P-value taken from randomization test of CCA. (B) Correlations between each pair of true canonical variates (CVs) against the distribution of maximum CV correlations from the randomization test (i.e. the null hypothesis). CV1 = the CV pair shown in A. Non-significant CV pairs are shaded grey. (C) Pearson’s correlation coefficients between each individual EEG feature and the first clinical canonical variate. Correlations that were not significant (P ≥ 0.05) are shaded grey. (D) Pearson’s correlation coefficients between each individual clinical variable and the first EEG canonical variate.

Figure 2

The prognostic value of relative alpha power. (Left) Tukey boxplot of relative alpha power for the healthy control group and the patient group. (Right) Residuals scatter plot of relationship between relative alpha power (mean across electrodes) and outcome at 3 months.