Bioenergetic Collapse and the Fragmenting Self: A Unified Attractor-State Model of Severe Psychopathology and Its Pharmacological Re-Architecture
Year: 2026
Type: preprint
Abstract: Neural functions are lost and recovered non-uniformly during anaesthesia, severe brain injury, and neurodegeneration. Whether a shared physiological constraint contributes to this ordering remains unresolved because connectivity, metabolism, and vascular reserve are measured separately. We conducted a structured critical narrative review of PubMed-indexed human PET, fMRI, EEG/TMS-EEG, stimulation, and longitudinal MRI studies, supplemented by reference-list checking and cellular energy-budget work. Evidence was grouped a priori into five domains: healthy connectivity-metabolism coupling; anaesthetic loss and emergence; perturbational capacity in disorders of consciousness; posteromedial vulnerability in Alzheimer's disease; and vascular or structural reserve. We separated direct observations from energetic interpretations and prospective predictions. Multimodal studies show that highly connected regions often have greater metabolic demand, but with regional heterogeneity. Anaesthesia and disorders of consciousness reveal state-dependent disruption and recovery of thalamocortical or frontoparietal interactions and perturbation-evoked complexity, whereas early Alzheimer's disease frequently involves posteromedial hypometabolism and reduced dynamic range. These convergences support testing an energetic-reserve hypothesis but do not establish a universal failure hierarchy: receptor pharmacology, neurovascular uncoupling, pathology topography, compensation, and connectivity preserved during deep anaesthesia remain alternatives. We define vulnerability as demand exceeding usable delivery and recruitable buffer over a specified time window—not as high resting glucose uptake, BOLD amplitude, or graph degree.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7203462316
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