Resumen:
Consciousness is one of the most complex aspects of human experience. Studying the mechanisms involved in transitions and in different levels of consciousness is one of the greatest challenges of neuroscience. In this study we use a measure based on computing the integrated information using an autoregressive system (ΦAR) to evaluate dynamic changes during consciousness transitions. This index is applied to an intracranial electroencephalography (iEEG) dataset collected from 6 patients that suffer from refractory epilepsy. These records were obtained during resting state and during the crisis. Networks outside epileptogenic regions were created in order to study the ΦAR index evolution when a crisis is detected. By using the Consciousness Seizure Scale (CCS) we show that changes on ΦAR are significantly correlated with changes in the reported states of consciousness.
