In humans, the psychedelic state induced by ketamine is associated with increased diversity and complexity of spontaneous electroencephalography (EEG) signals, but not of evoked EEG responses. This suggests that ketamine's effects on brain dynamics are specific to ongoing, internally generated neural activity rather than to stimulus-driven responses. The finding points to a potential neural marker of altered consciousness under psychedelics.
Consciousness is more complex than simply labeling states like deep sleep and anesthesia as "unconscious." Investigations show that behavioral signs and subjective reports can be unreliable, challenging traditional beliefs. For instance, individuals in presumed unconscious states may still report experiences, suggesting that 30-50% of patients under general anesthesia retain some awareness. Reevaluating these assumptions could enhance scientific clarity and improve clinical practices, fostering a deeper understanding of consciousness and its disorders. A nuanced approach is essential for future advancements in the field.
Integrated information theory (IIT) begins with phenomenology and predicts that only select physical substrates can support consciousness. The theory's account of experience—a cause-effect structure quantified by integrated information—has nothing to do with information transfer. This commentary outlines IIT's axioms and postulates, correcting major misconceptions from a target article that misrepresents the theory's foundations and ignores essential publications.
Measures that distinguish conscious from unconscious brain states may also be influenced by attentional load and cognitive resource use within conscious states. Testing several proposed measures, the study examines whether they are modulated by changes in attention and cognitive demands, which has rarely been tested before. The findings suggest that these measures are not solely markers of consciousness but can vary with attentional load within conscious states.
A systematic review of 255 EEG-based measures of consciousness found that signal diversity and event-related potential measures are the most consistent for distinguishing conscious from unconscious brain states. Spectral entropy, Lempel Ziv complexity, and spectral edge frequency emerged as the most practical, consistent, and reproducible measures. However, because most studies relied on behavioral assessments rather than subjective reports, the true presence or absence of phenomenological experience remains uncertain, limiting the conclusions that can be drawn. The review provides detailed categorizations to guide future research.