Through the collaboration of University Hospitals Birmingham and the University of Birmingham, we aim to use electroencephalography to investigate the complexity of the brain’s response to speech after a severe, traumatic, brain injury and thereby define more accurate markers for prognosis (i.e. predicted level of recovery). Traumatic brain injury (TBI) remains a leading cause of morbidity and mortality, particularly affecting the young and male. Although previously reported to be an untreatable cause of mortality, there have been improvements, particularly, when treatment is given in specialist neurosurgical centres, irrespective of need for surgical intervention as recommended by NICE in the UK. Some survivors, however, may subsequently develop a prolonged disorder of consciousness, such as the vegetative state, in which they do not communicate or show responses to speech. It is a significant challenge to accurately predict the level of recovery that each individual may achieve. Electroencephalography, or EEG, is a non-invasive form of brain-imaging that records the tiny electrical signals generated by the brain via a series of electrodes placed on the scalp. Brain responses to auditory stimuli have been shown to provide useful information for prognostication from coma. However, previous studies have used simple auditory stimuli like tones (i.e. beeps). Nevertheless, we know that the brain can process complex stimuli, like speech, even when unconscious, and that these responses contain rich information regarding the underlying brain networks. By measuring the brain’s response to speech, it may be possible to improve the accuracy of prognosis in coma. The EEG response to speech reflects the activity of a hierarchy of brain networks involved in both simple acoustic processing (i.e. what sort of sound is this?) as well as complex processing of meaning (i.e. what does this word mean in this context?). In the brain, the meanings of words are stored across the entire cortex (the surface of the brain). Therefore, specific kinds of words may be used to measure the relative preservation of brain networks of varying levels of complexity, and thereby the potential for those networks to support recovery subsequently.
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