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 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 distinct, empirically uncorrelated functional connectivity traits (“FC-Traits”), revealing two dissociable patterns: a primary trait whose expression scaled with plasma psilocin concentration, and a second, independent trait whose expression was associated with impaired performance on a visual divergent thinking task. These findings are consistent with the view that the acute psilocybin state involves co-occurring, dissociable connectivity patterns rather than a single global reconfiguration. This work demonstrates the potential of a decompositional connectomic framework to move beyond global descriptions and characterise dissociable connectivity patterns associated with distinct pharmacological and cognitive measures.