Yonatan Sanz Perl

2 months ago | nature.com | Juan Piccinini |Yonatan Sanz Perl |Gustavo Deco |Morten L. Kringelbach |David Nutt

AbstractThe transition towards the brain state induced by psychedelic drugs is frequently neglected in favor of a static description of their acute effects. We use a time-dependent whole-brain model to reproduce large-scale brain dynamics measured with fMRI from 15 volunteers under 20 mg intravenous N,N-Dimethyltryptamine (DMT), a short-acting psychedelic. To capture its transient effects, we parametrize the proximity to a global bifurcation using a pharmacokinetic equation.

Aug 5, 2024 | nature.com | Gustavo Deco |Yonatan Sanz Perl |Samuel Johnson |Robin Carhart-Harris |Morten L. Kringelbach |Niamh Bourke

Effective interventions for neuropsychiatric disorders may work by rebalancing the brain’s functional hierarchical organization. Here we directly investigated the effects of two different serotonergic pharmacological interventions on functional brain hierarchy in major depressive disorder in a two-arm double-blind phase II randomized controlled trial comparing psilocybin therapy (22 patients) with escitalopram (20 patients). Patients with major depressive disorder received either 2 × 25 mg of oral psilocybin, three weeks apart, plus six weeks of daily placebo (‘psilocybin arm’) or 2 × 1 mg of oral psilocybin, three weeks apart, plus six weeks of daily escitalopram (10–20 mg; ‘escitalopram arm’). Resting-state functional magnetic resonance imaging scans were acquired at baseline and three weeks after the second psilocybin dose ( NCT03429075 ). The brain mechanisms were captured by generative effective connectivity, estimated from whole-brain modeling of resting state for each session and patient. Hierarchy was determined for each of these sessions using measures of directedness and trophic levels on the effective connectivity, which captures cycle structure, stability and percolation. The results showed that the two pharmacological interventions created significantly different hierarchical reconfigurations of whole-brain dynamics with differential, opposite statistical effect responses. Furthermore, the use of machine learning revealed significant differential reorganization of brain hierarchy before and after the two treatments. Machine learning was also able to predict treatment response with an accuracy of 0.85 ± 0.04. Overall, the results demonstrate that psilocybin and escitalopram work in different ways for rebalancing brain dynamics in depression. This suggests the hypothesis that neuropsychiatric disorders could be closely linked to the breakdown in regions orchestrating brain dynamics from the top of the hierarchy. Psilocybin and escitalopram create significantly different reconfigurations in the global functional hierarchy of brain dynamics with opposite statistical effect responses in people with major depressive disorder.