Search

While electroencephalogram (EEG) burst-suppression is often induced therapeutically using sedatives in the intensive care unit (ICU), there is hitherto no evidence with respect to its association to outcome in moderate-to-severe neurological patients. We examined the relationship between sedation-induced burst-suppression (SIBS) and outcome at hospital discharge and at 6-month follow up in patients surviving moderate-to-severe traumatic brain injury (TBI). For each of 32 patients recovering from coma after moderate-to-severe TBI, we measured the EEG burst suppression ratio (BSR) during periods of low responsiveness as assessed with the Glasgow Coma Scale (GCS). The maximum BSR was then used to predict the Glasgow Outcome Scale extended (GOSe) at discharge and at 6 months post-injury. A multi-model inference approach was used to assess the combination of predictors that best fit the outcome data. We found that BSR was positively associated with outcomes at 6 months (P = 0.022) but did not predict outcomes at discharge. A mediation analysis found no evidence that BSR mediates the effects of barbiturates or propofol on outcomes. Our results provide initial observational evidence that burst suppression may be neuroprotective in acute patients with TBI etiologies. SIBS may thus be useful in the ICU as a prognostic biomarker.

Objectives: Considering sensory stimulation programs (SSP) as a treatment for disorders of consciousness is still debated today. Previous studies investigating its efficacy were affected by various biases among which small sample size and spontaneous recovery. In this study, treatment-related changes were assessed using time-series design in patients with disorders of consciousness (i.e., vegetative state—VS and minimally conscious state—MCS).Methods: A withdrawal design (ABAB) was used. During B phases, patients underwent a SSP (3 days a week, including auditory, visual, tactile, olfactory, and gustatory stimulation). The program was not applied during A phases. To assess behavioral changes, the Coma Recovery Scale-Revised (CRS-R) was administered by an independent rater on a weekly basis, across all phases. Each phase lasted 4 weeks. In a subset of patients, resting state functional magnetic resonance imaging (fMRI) data were collected at the end of each phase.Results: Twenty nine patients (48 ± 19 years old; 15 traumatic; 21 > a year post-injury; 11 VS and 18 MCS) were included in our study. Higher CRS-R total scores (medium effect size) as well as higher arousal and oromotor subscores were observed in the B phases (treatment) as compared to A phases (no treatment), in the MCS group but not in the VS group. In the three patients who underwent fMRI analyses, a modulation of metabolic activity related to treatment was observed in middle frontal gyrus, superior temporal gyrus as well as ventro-anterior thalamic nucleus.Conclusion: Our results suggest that SSP may not be sufficient to restore consciousness. SSP might nevertheless lead to improved behavioral responsiveness in MCS patients. Our results show higher CRS-R total scores when treatment is applied, and more exactly, increased arousal and oromotor functions.

In recent years, the study of the neural basis of consciousness, particularly in the context of patients recovering from severe brain injury, has greatly benefited from the application of sophisticated network analysis techniques to functional brain data. Yet, current graph theoretic approaches, as employed in the neuroimaging literature, suffer from four important shortcomings. First, they require arbitrary fixing of the number of connections (i.e., density) across networks which are likely to have different “natural” (i.e., stable) density (e.g., patients vs. controls, vegetative state vs. minimally conscious state patients). Second, when describing networks, they do not control for the fact that many characteristics are interrelated, particularly some of the most popular metrics employed (e.g., nodal degree, clustering coefficient)—which can lead to spurious results. Third, in the clinical domain of disorders of consciousness, there currently are no methods for incorporating structural connectivity in the characterization of functional networks which clouds the interpretation of functional differences across groups with different underlying pathology as well as in longitudinal approaches where structural reorganization processes might be operating. Finally, current methods do not allow assessing the dynamics of network change over time. We present a different framework for network analysis, based on Exponential Random Graph Models, which overcomes the above limitations and is thus particularly well suited for clinical populations with disorders of consciousness. We demonstrate this approach in the context of the longitudinal study of recovery from coma. First, our data show that throughout recovery from coma, brain graphs vary in their natural level of connectivity (from 10.4 to 14.5%), which conflicts with the standard approach of imposing arbitrary and equal density thresholds across networks (e.g., time-points, subjects, groups). Second, we show that failure to consider the interrelation between network measures does lead to spurious characterization of both inter- and intra-regional brain connectivity. Finally, we show that Separable Temporal ERGM can be employed to describe network dynamics over time revealing the specific pattern of formation and dissolution of connectivity that accompany recovery from coma.