Functional isolation within the cerebral cortex in the vegetative state: a nonlinear method to predict clinical outcomes.

BACKGROUND

Establishing prognosis in patients in a persistent vegetative state (VS) is still challenging. Neural networks underlying consciousness may be regarded as complex systems whose outputs show a degree of unpredictability experimentally quantifiable by means of nonlinear parameters such as approximate entropy (ApEn).

OBJECTIVE

The authors propose that the VS might be the result of derangement of the above neural networks, with an ensuing decrease in complexity and mutual interconnectivity: this might lead to a functional isolation within the cerebral cortex and to a reduction in the chaotic behavior of its outputs, with monotony taking the place of unpredictability. To test this hypothesis, the authors investigated whether nonlinear dynamics methods applied to electroencephalography (EEG) recordings may be able to predict outcomes.

METHODS

A total of 38 vegetative patients and 40 matched healthy controls were investigated. At admission, all patients were assessed by means of the Extended Glasgow Outcomes Coma Scale (E-GOS) and the Coma Recovery Scale-Revised (CRS-R). At the same time an EEG recording was performed and used for time series analysis and ApEn computation. Patients were clinically reassessed at 6 months from the first evaluation.

RESULTS

Mean ApEn values (0.73, standard deviation [SD] = 0.12 vs 0.97, SD = 0.02; P < .001) were lower in patients than in controls. Patients with the lowest ApEn values either died (n = 14) or remained in a VS (n = 12), whereas patients with the highest ApEn values became minimally conscious (n = 5) or showed partial (n = 4) or full recovery (n = 3).

CONCLUSIONS

These findings suggest that dynamic correlates of neural residual complexity might help in predicting outcomes in vegetative patients.