Re-appearance with proper timing of spontaneous motility, eye tracking and oculo-cephalic reflex and disappearance of oral automatisms proved highly correlated to outcome and allowed early and reliable prognosis. These findings are consistent with the brain functional organization thought to sustain consciousness and warrant systematic investigation. Classification and regression trees and data-mining procedures proved applicable in neurology to sort out significant clinical signs also in clinical conditions characterized by paucity of signs such as the vegetative state. Extended application in clinical medicine is conceivable based on the approach peculiarities.

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Activations to pleasant and unpleasant musical stimuli were observed within an extensive neuronal network and different brain structures, as well as in the processing of the syntactic and semantic aspects of the music. Previous studies evidenced a correlation between autonomic activity and emotion evoked by music listening in patients with Disorders of Consciousness (DoC). In this study, we analyzed retrospectively the autonomic response to musical stimuli by mean of normalized units of Low Frequency (nuLF) and Sample Entropy (SampEn) of Heart Rate Variability (HRV) parameters, and their possible correlation to the different complexity of four musical samples (i.e., Mussorgsky, Tchaikovsky, Grieg, and Boccherini) in Healthy subjects and Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) patients. The complexity of musical sample was based on Formal Complexity and General Dynamics parameters defined by Imberty's semiology studies. The results showed a significant difference between the two groups for SampEn during the listening of Mussorgsky's music and for nuLF during the listening of Boccherini and Mussorgsky's music. Moreover, the VS/UWS group showed a reduction of nuLF as well as SampEn comparing music of increasing Formal Complexity and General Dynamics. These results put in evidence how the internal structure of the music can change the autonomic response in patients with DoC. Further investigations are required to better comprehend how musical stimulation can modify the autonomic response in DoC patients, in order to administer the stimuli in a more effective way.

Background and purpose: Brain processing at varying levels of functional complexity and emotional reactions to relatives are anecdotally reported by the caregivers of patients in a vegetative state. In this study, computer-assisted machine-learning procedures were applied to identify heart rate variability changes or galvanic skin responses to a relative’s presence. Methods: The skin conductance (galvanic skin response) and heart beats were continuously recorded in 12 patients in a vegetative state, at rest (baseline) and while approached by a relative (usually the mother; test condition) or by a nonfamiliar person (control condition). The cardiotachogram (the series of consecutive intervals between heart beats) was analyzed in the time and frequency domains by computing the parametric and nonparametric frequency spectra. A machine-learning algorithm was applied to sort out the significant spectral parameter(s). For all patients, each condition (baseline, test, control) was characterized by the values of its spectral parameters, and the association between spectral parameters values and experimental condition was tested (WEKA machine-learning software). Results and comments: A galvanic skin response was obtained in two patients. The machine-learning procedure independently selected the nu_LF spectral parameter and attributed each nu_LF measure to any of the three experimental conditions. 69.4% of attributions were correct (baseline: 58%; test condition: 75%; control. 75%). In seven patients, attribution changed when the subject was approached by the test person; specifically, sequential shifts from baseline to test condition (“the Mom effect”) to control condition were identified in four patients (30.0%); the change from test to control was attributed correctly in seven patients (58%). The observation of heart rate changes tentatively attributable to emotional reaction in a vegetative state suggest residual rudimentary personal interaction, consistent with functioning limbic and paralimbic systems after massive brain damage. Machine-learning proved applicable to sort significant measure(s) out of large samples and to control for statistical alpha inflation.

In the 1998-2005 period, the incidence of PSH was 32% and 16% in post-traumatic and non-traumatic patients, respectively. It decreased to 18% and 7% in the 2006-2010 period. The PSH duration and the time spent in emergency units before admission and in the dedicated unit for the vegetative state after admission also decreased significantly. Incidence was greater among post-traumatic- patients; its effect on outcome does not appear to have changed.