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.

PRIMARY OBJECTIVE: To challenge the Multi-Society Task Force's ruling that a persistent vegetative state (PVS) can be judged to be permanent for non traumatic brain injury after three months. METHOD: We report the case of a 44-year-old man who had recovery of consciousness with persistent severe disability 19 months after a non-traumatic brain injury at least in part triggered and maintained by intrathecal baclofen administration. RESULT: This unexpected and late recovery of consciousness raises an interesting hypothesis of possible effects of partially regained spinal cord outputs on reactivation of cognition. CONCLUSION: Considering that several ethical, legal, and socio-economic issues have been raised about the opportunity of withdrawing treatment and life support in patients with PVS the report of this case might add further documentation to the ongoing debate

This study evaluated the hypothesis that neural networks derangement in patients with a vegetative state (VS) may cause an alteration of heart rate (HR) non-linear pattern. Fifteen consecutive patients with a persistent VS and 15 matched healthy control subjects were included in the study. A 6-hour continuous electrocardiographic recording was used for the time series analysis measuring the occurrence time of the intervals between consecutive normal sinus heart beats (RR' intervals). Parameters evaluating linear and non-linear HR variability were studied. Approximate Entropy (ApEn), a non-linear parameter that quantifies the unpredictability of fluctuations in an instantaneous HR time series, was calculated from the average values of time series with fixed input variables. All linear parameters, with the only exception being the percentage of RR' intervals that were by at least 50 ms different from the previous interval (0.56, SD = 1.31 vs 10.35, SD = 12.58; p = 0.005) were similar in patients and in healthy control subjects. Mean ApEn values (0.68, SD = 0.24 vs 1.10, SD = 0.16; p = 0.0001) were lower in patients than in healthy control subjects. The findings support the hypothesis that derangement of neural networks may cause a reduction of non-linear behaviour in HR such as ApEn.

The experience of pain in disorders of consciousness is still debated. Neuroimaging studies, using functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), multichannel electroencephalography (EEG) and laser-evoked potentials, suggest that the perception of pain increases with the level of consciousness. Brain activation in response to noxious stimuli has been observed in patients with unresponsive wakefulness syndrome (UWS), which is also referred to as a vegetative state (VS), as well as those in a minimally conscious state (MCS). However, all of these techniques suggest that pain-related brain activation patterns of patients in MCS more closely resemble those of healthy subjects. This is further supported by fMRI findings showing a much greater functional connectivity within the structures of the so-called pain matrix in MCS as compared to UWS/VS patients. Nonetheless, when interpreting the results, a distinction is necessary between autonomic responses to potentially harmful stimuli and conscious experience of the unpleasantness of pain. Even more so if we consider that the degree of residual functioning and cortical connectivity necessary for the somatosensory, affective and cognitive-evaluative components of pain processing are not yet clear. Although procedurally challenging, the particular value of the aforementioned techniques in the assessment of pain in disorders of consciousness has been clearly demonstrated. The study of pain-related brain activation and functioning can contribute to a better understanding of the networks underlying pain perception while addressing clinical and ethical questions concerning patient care. Further development of technology and methods should aim to increase the availability of neuroimaging, objective assessment of functional connectivity and analysis at the level of individual cases as well as group comparisons. This will enable neuroimaging to truly become a clinical tool to reliably investigate pain in severely brain-injured patients as well as an asset for research.