Network Neuroscience
November 1, 2023
Juan Carlos Farah, Pablo Mallaroni, Enrico Amico et al.
21 citations
Functional connectomes become more idiosyncratic under psilocybin, with greater dissimilarity between individuals than under placebo. While idiosyncratic features in placebo subjects appear mainly in the frontoparietal network, under psilocybin they concentrate in the default mode network (DMN). A DMN-focused pattern predicts subjective psilocybin experience, marked by reduced within-DMN and DMN-limbic connectivity and increased connectivity between the DMN and attentional systems. These findings link psilocybin's brain effects to subjective experience and demonstrate the value of brain-fingerprinting in pharmacological neuroimaging.
bioRxiv (Cold Spring Harbor Laboratory)
October 11, 2022
Pablo Mallaroni, Natasha L. Mason, Lilian Kloft et al.
4 citations
preprint
Brain functional connectomes are unique and reliable identifiers of individuals, but it was unknown whether these 'fingerprints' persist during altered states of consciousness. Ayahuasca, a serotonergic psychedelic, disrupts functional connectivity. In a within-subject study using 7T fMRI, 21 members of the Santo Daime church were scanned after collective ayahuasca intake. Connectome fingerprinting revealed a shared functional space and a spatiotemporal reallocation of key edges. Differences in higher-order functional connectivity motifs predicted perceptual drug effects, showing that individualized connectivity markers can trace a subject's functional connectome across altered states.
bioRxiv (Cold Spring Harbor Laboratory)
March 21, 2023
Hanna M. Tolle, Juan Carlos Farah, Pablo Mallaroni et al.
1 citation
preprint
Functional connectomes (FCs) become more idiosyncratic under the psychedelic psilocybin than under placebo, with idiosyncratic features concentrating in the default-mode network (DMN). An FC pattern predicting subjective psilocybin experience shows reduced within-DMN and DMN-limbic connectivity, alongside increased DMN-attentional system connectivity. These results bridge psilocybin's brain effects and behavior, demonstrating the value of brain-fingerprinting in pharmacological neuroimaging.
bioRxiv (Cold Spring Harbor Laboratory)
Hanna M. Tolle, Andrea I Luppi, Timothy Lawn et al.
1 citation
preprint
A geometric deep learning model called graphTRIP predicts post-treatment depression severity from pretreatment clinical and brain imaging data. Trained on a clinical trial comparing psilocybin and escitalopram, it achieves strong predictive accuracy (r = 0.75) and generalizes to an independent dataset. The model links better outcomes to reduced functional coupling within serotonin systems and broader serotonergic integration with sensory-motor networks. Causal analysis shows a group-level advantage of psilocybin over escitalopram but identifies individuals with specific stress-related neuromodulatory profiles who may benefit more from escitalopram, advancing precision medicine and biomarker discovery in depression.