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Anesthesia via Dissociative Overload: A Unified Theoretical Framework Linking DMT and Ketamine Through Sigma-1 Convergence, Entropic Saturation, and Workspace Flooding

M. Hawarey

November 3, 2026 DOI: 10.65737/airlsm2026698 (opens in new tab)

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AI-extracted from the abstract
Characteristics Theoretical or philosophical paper
Topics DMT Esketamine Ketamine
Key points Argues that DMT and ketamine achieve clinical unresponsiveness through informational saturation rather than neural suppression, converging on Sigma-1 receptor and prefrontal glutamate mechanisms despite distinct primary targets, and proposes a dual taxonomy of Suppressive versus Saturative anesthesia with six falsifiable predictions. The authors state the framework is empirically grounded for ketamine but that its extension to DMT-induced anesthesia is a central prediction awaiting direct experimental validation.

Abstract

General anesthesia has long been understood as a unitary phenomenon driven by neural suppression — a reduction in cortical activity, entropy, and complexity that renders the brain unable to sustain conscious processing. Yet two structurally and pharmacologically distinct compounds, N,N-Dimethyltryptamine (DMT) and ketamine, produce clinical unresponsiveness while maintaining or increasing cortical complexity, a pattern fundamentally incompatible with the suppression paradigm. No existing theoretical framework explains how these orthogonal compounds converge on a common anesthetic mechanism that operates through informational excess rather than deprivation. Here, we propose a novel unified framework for “Anesthesia via Dissociative Overload,” arguing that DMT and ketamine achieve unresponsiveness not by silencing neural activity but by saturating cortical information processing beyond the capacity for coherent external-world modeling. The framework integrates three levels of analysis: (1) molecular convergence on the Sigma-1 receptor (σ1R) and a prefrontal glutamate surge as shared pharmacological endpoints despite distinct primary targets (5-HT2A vs. NMDA); (2) entropic saturation, whereby maintained or elevated neural entropy during unresponsiveness reflects informational overload rather than a monitoring artifact; and (3) workspace flooding, in which mass cortical activation overwhelms the Global Neuronal Workspace’s serial bottleneck, collapsing the predictive coding hierarchy and preventing coherent broadcasting of external stimuli. This framework proposes a dual taxonomy of anesthesia — Suppressive (GABAergic, entropy-reducing) versus Saturative (glutamatergic/serotonergic, entropy-preserving) — and generates six falsifiable predictions distinguishing the two modes. The Dissociative Overload model reframes general anesthesia as a family of states unified by the failure to model external reality, achievable through either information deprivation or information saturation. While the framework is empirically grounded for ketamine, its extension to DMT-induced anesthesia constitutes a central prediction awaiting direct experimental validation.

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