Srivas Chennu

Title: Longitudinal Bedside Assessments of Brain Networks Track Individual Recovery in Disorders of Consciousness

Abstract: Clinicians are regularly faced with the difficult challenge of diagnosing consciousness after severe brain injury. As such, as many as 40% of minimally conscious patients who demonstrate fluctuations in arousal and awareness are known to be misdiagnosed as unresponsive/vegetative. Further, a significant minority of patients show evidence of hidden awareness not evident in their behaviour. Despite this, behavioural assessments, in particular, the Coma Recovery Scale-Revised (CRS-R), are the most commonly used bedside measures of consciousness. However, recent advances in functional high-density electroencephalography (hdEEG) have indicated that specific patterns of resting brain connectivity measured at the bedside are strongly correlated with the re-emergence of consciousness after brain injury. Building upon this research, we present findings from three patients with traumatic brain injury who underwent regular assessments of hdEEG connectivity and Coma Recovery Scale-Revised (CRS-R) at the bedside, as part of an ongoing longitudinal study. The first, a patient in an unresponsive wakefulness state, progressed to a minimally-conscious state several years after injury. hdEEG measures of alpha network centrality in this patient tracked this behavioural improvement. The second patient, diagnosed as minimally conscious minus, demonstrated a significant late increase in behavioural awareness to minimally conscious plus. This patient’s hdEEG connectivity across the previous 18 months showed a trajectory consistent with this increase alongside a decrease in delta power. Finally, the third minimally conscious plus patient emerged from minimal consciousness. In this patient, decrease in the centrality of delta connectivity networks was associated with emergence from minimal consciousness. Across these contrasting cases spanning progressively higher behavioural transitions, hdEEG connectivity captures recovery trajectories both within and between patients. Our findings demonstrate that bedside hdEEG assessments can complement clinical evaluation with portable, accurate and timely generation of brain-based patient profiles to inform diagnosis and prognosis. Further, such hdEEG assessments could be used to estimate the potential utility of complementary neuroimaging assessments, and to evaluate the efficacy of interventions.