Recent research suggests that the detection of preserved cognitive function can assist in the diagnosis and prognosis of patients with disorders of consciousness (DoC). This study investigates EEG signals as indicators of neural activity associated with the processing of transitional probabilities during a learning paradigm in patients with DoC. By examining the sensitivity to transitional probabilities across levels of consciousness, we aim to assess the potential value of this indicator in clinical diagnosis and prognosis. We collected EEG recordings from 51 DoC patients (10 female) and 26 healthy controls (9 female). EEG activity was recorded while participants listened to artificial vocabulary speech sequences before and after the learning phase. Intertrial phase coherence (ITPC) was used to examine differences in neural responses in different learning phases. Results showed that minimally conscious patients showed a significant increase in the word-tracking response after the learning phase, similar to healthy controls. Moreover, their learning-mediated word-rate ITPC difference correlated significantly with their Coma Recovery Scale-Revised score and 6 month outcome. However, these correlations were absent in unresponsive wakefulness syndrome patients. Crucially, differences in vocabulary ITPC before and after the learning phase effectively discriminated between healthy controls and patients, as well as between minimally conscious and unresponsive wakefulness syndrome patients. Combining EEG indicators with clinical performance accurately predicted patients' prognosis. In conclusion, the language learning paradigm has the potential to contribute to both diagnosis and prognosis in this challenging population, thereby significantly reducing prognostic uncertainty in medical decision-making and benefiting the rehabilitation of DoC patients.
Keywords: diagnosis; disorders of consciousness; electroencephalogram; explicit learning; language; prognosis.
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