Rebalancing the depressed brain: a wholebrain computational study on the effects of external perturbations in psilocybin and escitalopram treatments

Abstract

Serotonergic psychedelics like psilocybin have been proposed as a promising avenue in the treatment of major depressive disorder (MDD). Contrary to selective serotonin reuptake inhibitors (SSRI) like escitalopram, psychedelics work through 5-HT2AR agonism, which opens a window of plasticity for psychotherapy- or neurostimulation-combined therapies. Here, we used a trial comparing psilocybin and escitalopram treatments for MDD to assess in silico the effect of external perturbations after pharmacological intervention. The trial included resting state fMRI scans before and one day after treatment. We built whole-brain models by fitting the data to capture the underlying causal brain mechanisms in generative effective connectivity. Then, we applied a perturbation protocol following the Dynamic Sensitivity Analysis paradigm, simulating the effects of external stimulations in parcellated regions. We evaluated the impact of perturbations by assessing regional susceptibility to change and effectivity to drive a healthy transition (both defined using static functional connectivity). We show that susceptibility is enhanced by psilocybin and reduced by escitalopram, evidencing an opening of a window of plasticity by psilocybin. However, healthy transitions are similarly achieved after both treatments, suggesting that escitalopram manages to improve perturbation effects despite differences in functional hierarchies compared to psilocybin. We elaborate that longer-term data and measures capturing the temporal richness of functional repertoires might be needed to complement these findings. Finally, we demonstrate the benefits of multi-site versus single-site perturbations and that the amygdala and the nucleus accumbens are the best perturbation targets to drive healthy transitions. Overall, the present work contributes to prior knowledge on how in silico perturbations can help in the treatment of MDD, serving as a foundation for larger computational studies.