Back

Critical dynamics in spontaneous EEG predict perturbational complexity in disorders of consciousness with measurable evoked responses

Newman, D.; Maschke, C.; O'Byrne, J.; Colombo, M. A.; Comanducci, A.; Casarotto, S.; Citerio, G.; Rosanova, M.; Massimini, M.; Jerbi, K.; Blain-Moraes, S.

2026-06-09 neuroscience
10.64898/2026.06.04.730221 bioRxiv
Show abstract

Identifying which severely brain-injured patients retain the capacity for consciousness remains a major challenge in neurocritical care. The perturbational complexity index (PCI) provides a reliable assessment of consciousness capacity, but its reliance on transcranial magnetic stimulation and EEG (TMS-EEG) limits bedside scalability. PCI and brain criticality capture complementary dimensions of brain dynamics: PCI quantifies the complexity of the brains evoked response to perturbation, whereas criticality characterizes the intrinsic organization of spontaneous activity. Here, we tested whether resting-state EEG signatures of criticality predict PCImax in disorders of consciousness, extending prior findings from anesthesia to severe brain injury. In 26 patients with vascular, traumatic, or anoxic brain injury, multivariate criticality related features did not generalize PCImax prediction across the full heterogeneous cohort. However, criticality features predicted PCImax when analyses were restricted to non-anoxic patients and when restricting analyses to patients with non-zero PCImax values. These findings suggest that spontaneous criticality measures index the brains intrinsic dynamical regime that supports complex perturbational responses, while their correspondence with PCImax depends on whether the injured brain retains sufficient capacity to sustain large-scale evoked responses. Together, our results extend the relationship between resting-state criticality and evoked perturbational complexity to disorders of consciousness and support the development of stratified EEG measures in severe brain injury.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.