Classification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression
Nature Communications – February 12, 2025
Source: OpenAlex
Summary
A novel neuroscience approach accurately classifies psychoactive drugs, showing promise for future medicine. Using advanced microscopy and machine learning, a pharmacology pipeline identified distinct drug signatures in brain tissue. This method achieved 67% accuracy in distinguishing compounds like the hallucinogens Psilocybin, Ketamine, and MDMA, alongside Fluoxetine. Psilocybin was discriminated from other drugs with over 95% accuracy. Such precise drug studies advance our understanding of neurotransmitter receptor influence on behavior, critical for developing new treatments for brain disorders.
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.