Unmixing the Psychedelic Connectome: Brain Network Traits of Psilocybin
Krishna Prasad Bhavaraju, Natasha L. Mason, Pablo Mallaroni, Dietmar Heinke, Stefan W. Toennes, Johannes G. Ramaekers, Enrico Amico
November 17, 2025 preprint DOI: 10.1101/2025.11.17.688834 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study |
|---|---|
| Population | Healthy volunteers |
| Intervention | Psilocybin |
| Topics | Neuroplasticity Psilocybin |
| Keywords | Trait Cognition Neuroimaging Functional connectivity Nerve net Human connectome project Cognitive psychology Brain mapping Biological neural network Artificial neural network Independent component analysis Perspective graphical Neural activity Neurocognitive |
| Key findings | The acute psilocybin state is a composite of co-occurring neural processes, with one functional connectivity trait linked to plasma psilocin concentration and another independently associated with impaired visual divergent thinking performance. |
Abstract
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.