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The Emerging Neurobiology of Psychedelics: Critical Periods, Metaplasticity, and Extracellular Matrix Remodeling.

Gül Dölen, Makenzie L Wilkinson

Annual review of neuroscience July 1, 2026 DOI: 10.1146/annurev-neuro-112723-045129 via PubMed

Summary

AI-generated from the abstract

Psychedelics reopen critical periods, induce metaplasticity, and reorganize the extracellular matrix, which helps explain their diverse, durable, and context-dependent therapeutic effects. This neurobiological evidence challenges the biochemical imbalance model that has dominated translational neuroscience since the 1950s and supports a learning model that better accounts for psychedelics' unique therapeutic profile.

Study at a glance

Characteristics Review Peer reviewed
Keywords Ecm Critical period Extracellular matrix Learning Metaplasticity
Key finding A unifying property of psychedelics is that they reopen critical periods, induce metaplasticity, and reorganize the extracellular matrix, supporting a learning model over the biochemical imbalance model.

Abstract

Psychedelics are a broad category of compounds that induce altered states of consciousness. These drugs have shown remarkable promise for the treatment of debilitating disorders ranging from posttraumatic stress disorder to depression and addiction. Although early studies focused on linking binding targets of psychedelics to their therapeutic effects, these pharmacological and biochemical explanations fail to account for the diversity, durability, and context dependence of psychedelics' clinical and acute subjective effects. More recently, neurobiological explanations offer fresh insights and demonstrate that a unifying property of psychedelics is that these compounds reopen critical periods, induce metaplasticity, and reorganize the extracellular matrix. Here we review this evidence and argue that the neurobiological and therapeutic effects of psychedelics challenge the biochemical imbalance model that has dominated translational neuroscience since the 1950s and favor instead a learning model that better accounts for psychedelics' unique therapeutic profile.

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