The role of etiology in the identification of clinical markers of consciousness: comparing EEG alpha power, complexity, and spectral exponent

In the search for EEG markers of human consciousness, alpha power has long been considered a reliable marker which is fundamental for the assessment of unresponsive patients from all etiologies. However, recent evidence questioned the role of alpha power as a marker of consciousness and proposed the spectral exponent and spatial gradient as more robust and generalizable clinical indexes. In this study, we analyzed a large-scale dataset of 303 unresponsive patients and investigated etiology-specific differences in clinical markers of level of consciousness, responsiveness and capacity to recover. We compare a set of candidate EEG makers: i) absolute, relative and flattened alpha power; ii) spatial ratios; iii) the spectral exponent; and iv) signal complexity. Our results support the claim that alpha power has higher diagnostic value for anoxic patients. Meanwhile, the spectral slope showed diagnostic value for non-anoxic patients only. Changes in relative power and signal complexity occurred alongside changes in the spectral slope. Grouping unresponsive patients from different etiologies together can confound or obscure the diagnostic value of different EEG markers of consciousness. Our study highlights the importance of analyzing different etiologies independently and emphasizes the need to develop clinical markers which better account for inter-individual and etiology-dependent differences.

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Publication Details

Journal
Cerebral Cortex
Published
2025-08-20
DOI
https://doi.org/10.1093/cercor/bhaf254
Citations
2
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
2.86
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article

The role of etiology in the identification of clinical markers of consciousness: comparing EEG alpha power, complexity, and spectral exponent

Jacobo Diego Sitt, Charlotte Maschke, Laouen Belloli, Stefanie Blain‐Moraes et al.
2 citations
Cerebral Cortex
EEG and Brain-Computer Interfaces
2.86
article

The role of etiology in the identification of clinical markers of consciousness: comparing EEG alpha power, complexity, and spectral exponent

Jacobo Diego Sitt, Charlotte Maschke, Laouen Belloli, Stefanie Blain‐Moraes, Dragana Manasova
article en
2 citations

Abstract

In the search for EEG markers of human consciousness, alpha power has long been considered a reliable marker which is fundamental for the assessment of unresponsive patients from all etiologies. However, recent evidence questioned the role of alpha power as a marker of consciousness and proposed the spectral exponent and spatial gradient as more robust and generalizable clinical indexes. In this study, we analyzed a large-scale dataset of 303 unresponsive patients and investigated etiology-specific differences in clinical markers of level of consciousness, responsiveness and capacity to recover. We compare a set of candidate EEG makers: i) absolute, relative and flattened alpha power; ii) spatial ratios; iii) the spectral exponent; and iv) signal complexity. Our results support the claim that alpha power has higher diagnostic value for anoxic patients. Meanwhile, the spectral slope showed diagnostic value for non-anoxic patients only. Changes in relative power and signal complexity occurred alongside changes in the spectral slope. Grouping unresponsive patients from different etiologies together can confound or obscure the diagnostic value of different EEG markers of consciousness. Our study highlights the importance of analyzing different etiologies independently and emphasizes the need to develop clinical markers which better account for inter-individual and etiology-dependent differences.

Cerebral CortexVol. 35(9)
Centre National de la Recherche Scientifique (FR), Inserm (FR), Université Paris Cité (FR), Montreal General Hospital (CA), Sorbonne Université (FR), Sorbonne Paris Cité (FR), Assistance Publique – Hôpitaux de Paris (FR), Ministerio de Ciencia, Tecnología e Innovación (AR), Institut du Cerveau (FR), McGill University (CA)
Openalex Percentile: Top 8%
EEG and Brain-Computer Interfaces
2.86
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