bioRxivNeurosciencePreprint
Critical dynamics in spontaneous EEG predict perturbational complexity in disorders of consciousness with measurable evoked responses
Derek Newman, Charlotte MaschkeiD, Jordan O’ByrneiD, Michele Angelo Colombo, Angela ComanducciiD, Silvia CasarottoiD, Giuseppe CiterioiD, Mario RosanovaiD, Marcello MassiminiiD, Karim JerbiiD, Stefanie Blain‐MoraesiD
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.
- organism: human
- assay: electronic health records
- research focus: benchmarking
- approach: statistical model
- Citations
- 1
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