Unmixing the psychedelic connectome: Brain network traits of psilocybin

Abstract Psilocybin induces profound alterations in consciousness, yet prevailing neural models often describe a monolithic change in brain connectivity that may not fully capture the multifaceted nature of the psychedelic state. To test the hypothesis of a composite neural state, this study applied a robust, data-driven framework, Connectome Independent Component Analysis (connICA) with multi-level resampling, to resting-state fMRI data from healthy volunteers. The analysis decomposed connectomes into statistically independent functional connectivity traits ("FC-Traits"), revealing a primary trait whose expression was significantly modulated by plasma psilocin concentration, providing a whole-cortical signature of the drug’s physiological action. Crucially, a second, distinct trait was also isolated, which independently associated with impaired performance on a visual divergent thinking task. These findings demonstrate that the acute psilocybin state is a composite of co-occurring neural processes. This validates the application of a decompositional connectomic framework to move beyond global descriptions and successfully disentangle the specific neural patterns underlying distinct pharmacological and cognitive correlates.