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Classification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression

Farid Aboharb, Pasha A. Davoudian, Ling-Xiao Shao, Clara Liao, Gillian N. Rzepka, Cassandra Wojtasiewicz, Jonathan Indajang, Mark Dibbs, Jocelyne Rondeau, Alexander M. Sherwood, Alfred P. Kaye, Alex C. Kwan

Nature Communications February 12, 2025 DOI: 10.1038/s41467-025-56850-6 (opens in new tab)

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Study at a glance

AI-extracted from the abstract
Characteristics Preclinical experimental study Peer reviewed
Population Male and female mice
Interventions Psilocybin Ketamine 5-MeO-DMT 6-fluoro-DET MDMA acute fluoxetine chronic fluoxetine vehicle
Topics Ketamine MDMA Psilocybin
Keywords Pharmacology Fluoxetine Drug Hallucinogen
Citations 19
Key findings A machine-learning pipeline using immediate early gene expression in mouse brain tissues classified psychoactive drugs with 67% accuracy and discriminated psilocybin from other drugs with over 95% accuracy.

Abstract

Psilocybin, ketamine, and MDMA are psychoactive compounds that exert behavioral effects with distinguishable but also overlapping features. The growing interest in using these compounds as therapeutics necessitates preclinical assays that can accurately screen psychedelics and related analogs. We posit that a promising approach may be to measure drug action on markers of neural plasticity in native brain tissues. We therefore developed a pipeline for drug classification using light sheet fluorescence microscopy of immediate early gene expression at cellular resolution followed by machine learning. We tested male and female mice with a panel of drugs, including psilocybin, ketamine, 5-MeO-DMT, 6-fluoro-DET, MDMA, acute fluoxetine, chronic fluoxetine, and vehicle. In one-versus-rest classification, the exact drug was identified with 67% accuracy, significantly above the chance level of 12.5%. In one-versus-one classifications, psilocybin was discriminated from 5-MeO-DMT, ketamine, MDMA, or acute fluoxetine with >95% accuracy. We used Shapley additive explanation to pinpoint the brain regions driving the machine learning predictions. Our results suggest a unique approach for characterizing and validating psychoactive drugs with psychedelic properties.

Comparable studies

Other preclinical and animal studies on MDMA, most cited first.

Study Year Design Participants
Neurotoxicity of the psychedelic amphetamine, methylenedioxymethamphetamine. Rats (implied by typical neurochemical study context) 1987 Experimental study
3,4-Methylenedioxymethamphetamine and 3,4-methylenedioxyamphetamine destroy serotonin terminals in rat brain: quantification of neurodegeneration by measurement of [3H]paroxetine-labeled serotonin uptake sites. Rat brain 1987 Animal experiment
Comparison of the monoamine transporters from human and mouse in their sensitivities to psychostimulant drugs Human and mouse dopamine, norepinephrine, and serotonin transporters expressed in cells 2006 In vitro laboratory study
Small Changes in Ambient Temperature Cause Large Changes in 3,4-Methylenedioxymethamphetamine (MDMA)-Induced Serotonin Neurotoxicity and Core Body Temperature in the Rat Rats 1998 Experimental study
Altered Serotonin Innervation Patterns in the Forebrain of Monkeys Treated with (±)3,4-Methylenedioxymethamphetamine Seven Years Previously: Factors Influencing Abnormal Recovery Squirrel monkeys treated with MDMA 1999 Observational cohort

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Ketamine MDMA Psilocybin 5-MeO-DMT DMT Neuroplasticity