A brain-based measure called the Perturbational Complexity Index (PCI) can reliably distinguish conscious from unconscious individuals, even when they cannot speak or move. The index was first validated in 150 healthy and brain-injured people who could report their conscious state, achieving perfect accuracy in separating conscious from unconscious conditions. Applied to 81 noncommunicative patients, PCI correctly identified 94.7% of those in a minimally conscious state and revealed that 9 of 43 patients diagnosed as vegetative had PCI values overlapping with conscious individuals. These findings suggest that some behaviorally unresponsive patients may retain hidden conscious capacity.
Directing attention toward the body's internal signals (interoception) versus external sounds (exteroception) produces distinct brain activity patterns. Exteroceptive attention flattened overall brain wave power, while interoceptive attention reduced brain signal complexity, increased frontal connectivity and theta oscillations, and modulated the heartbeat-evoked potential (HEP). Classifiers using HEP features correctly identified the attentional state in 17 of 20 healthy participants; power spectral density features classified all 20. In five brain-injured patients, one with unresponsive wakefulness syndrome and one with locked-in syndrome showed willful modulation of the HEP, suggesting they could follow commands. These findings highlight how attention shapes sensory processing and may aid diagnosis in disorders of consciousness.
Among patients with prolonged disorders of consciousness admitted to Italian intensive rehabilitation units, a higher score on the visual sub-scale of the Coma Recovery Scale-Revised and the presence of EEG reactivity to eye opening at admission were the best independent predictors of complete recovery of consciousness (emergence from Minimally Conscious State) three months later. Of 131 patients who completed follow-up, 77 emerged from MCS, though most remained severely disabled. The findings suggest that multimodal assessment can identify patients likely to achieve functionally relevant improvements.