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Electrophysiological Signatures of Alpha Coma
Giulio Degano1, Francesco Misirocchi1,2, Isotta Rigoni3
1Department of Intensive Care, Neuro-Intensive Care Unit, University Hospital of Geneva, Geneva, Switzerland.
Purpose:
Recent research on quantitative EEG in coma has proposed several metrics correlating with consciousness level. However, the heterogeneous nature of coma can challenge the generalizability of these measures. This study investigates alpha-coma, an electroclinical pattern characterized by a widespread, nonreactive alpha rhythm often linked to poor outcomes. The aim was to quantify the electrophysiological features of alpha-coma and compare them to the alpha rhythm in awake controls, seeking clearer insights into quantitative EEG analysis in comatose states.
Methods:
Fourteen alpha-coma patients were retrospectively selected from University Hospitals of Geneva and age-matched with 14 healthy control subjects from an open-source dataset. EEG data were preprocessed and analyzed to extract power spectra, spectral decay (aperiodic activity), sample entropy, and functional connectivity.
Results:
Alpha-coma patients did not differ in alpha power but exhibited significantly higher levels of spectral decay ( p < 0.001), suggesting a convergence toward an inhibitory state. Sample entropy was significantly higher in alpha-coma patients ( p = 0.01), indicating an increase in the cortical complexity in alpha-coma compared with healthy subjects.
Conclusions:
Alpha-coma shows increased aperiodic activity and EEG complexity, despite similar alpha power and clustering coefficient. The increased aperiodic activity aligns with findings in other comatose patients, including those sedated or with subcortical dysfunction. However, the increased entropy contradicts existing literature, suggesting that alpha-coma may represent a state of widespread cortical dysfunction likely resulting from nonhierarchical, turbulent brain activity. This indicates that the loss of consciousness does not guarantee consistent cortical measures across the whole spectrum of EEG patterns.
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