Managing disorders of consciousness: the role of electroencephalography

Abstract

Disorders of consciousness (DOC) are an important but still underexplored entity in neurology. Novel electroencephalography (EEG) measures are currently being employed for improving diagnostic classification, estimating prognosis and supporting medicolegal decision-making in DOC patients. However, complex recording protocols, a confusing variety of EEG measures, and complicated analysis algorithms create roadblocks against broad application. We conducted a systematic review based on English-language studies in PubMed, Medline and Web of Science databases. The review structures the available knowledge based on EEG measures and analysis principles, and aims at promoting its translation into clinical management of DOC patients.

Keywords: Disorders of consciousness; Electroencephalography; Event-related potential; Minimally conscious state; TMS-EEG; Unresponsive wakefulness state; Vegetative state.

Fig. 1

Main EEG measures obtained in patients with disorders of consciousness: rest-state EEG, sleep EEG, event-related potentials (ERP), brain-computer interface (BCI) and transcranial magnetic stimulation–electroencephalography (TMS-EEG). These measures provide various EEG characteristics based on different information extraction algorithms. The right panel summarizes the main EEG characteristics with respect to their potential values in classifying minimally conscious state (MCS) and vegetative state/unresponsive wakefulness syndrome (VS/UWS) (diagnostic value), outcomes prediction (prognostic value) and treatment monitoring (response to treatment). The colors identify EEG measures belonging to the main EEG techniques as indicated by the same colors in the left panel. ± in the ‘diagnostic value’ column indicate that EEG measures are larger (+)/smaller (–) in MCS than in VS/UWS. ± in the ‘prognostic value’ column indicate that larger EEG measures correspond to better (+)/worse (–) outcome. Finally, ± in the ‘response to treatment’ column means that EEG measures increased/decreased during treatment. KCC Kolmogorov–Chaitin complexity, wSMI weighted symbolic mutual information, dwPLI debiased weighted phase lag index, STEn symbolic transfer entropy, LPC late positive components, VMI visuomotor integration, VEP visual evoked potentials, MMN mismatch negativity, TEP TMS evoked potentials, GMFP global mean field power, PCI perturbational complexity index

Fig. 2

Perturbational complexity index (PCI) discriminates the level of consciousness in brain-injured patients. a PCI values for 48 TMS sessions collected from 20 severely brain-injured patients (TMS was targeted to both left and right Brodmann areas 8 and 7, as indicated at top left). Right: distribution of PCI values from healthy individuals. b Box plots for PCI in brain-injured patients with the statistical significance between pairs of conditions (Linear mixed-effects models: *P = 0.002, **P = 0.0001, ***P = 2 × 10⁻⁵, ****P = 8 × 10⁻⁷). Gray and red dashed lines in (a) and (b) represent the maximum complexity observed during unconsciousness (PCI = 0.31) and the minimum complexity observed during alert wakefulness (PCI = 0.44) in healthy subjects, respectively (with permission, from Casali et al. [110] Sci Transl Med 5:198ra105)