Brain dynamics predictive of response to psilocybin for treatment-resistant depression
Jakub Vohryzek, Joana Cabral, Louis-David Lord, Henrique M. Fernandes, Leor Roseman, David Nutt et al. · 2022 · 9 citationsRead the paper
Abstract Psilocybin therapy for depression has started to show promise, yet the underlying causal mechanisms are not currently known. Here we leveraged the differential outcome in responders and non-responders to psilocybin (10mg and 25mg, 7 days apart) therapy for depression - to gain new insights into regions and networks implicated in the restoration of healthy brain dynamics. We used whole-brain modelling to fit the spatiotemporal brain dynamics at rest in both responders and non-responders before treatment. Dynamic sensitivity analysis of systematic perturbation of these models enabled us to identify specific brain regions implicated in a transition from a depressive brain state to a heathy one. Binarizing the sample into treatment responders (>50% reduction in depressive symptoms) versus non-responders enabled us to identify a subset of regions implicated in this change. Interestingly, these regions correlate with in vivo density maps of serotonin receptors 5-HT 2A and 5-HT 1A , which psilocin, the active metabolite of psilocybin, has an appreciable affinity for, and where it acts as a full-to-partial agonist. Serotonergic transmission has long been associated with depression and our findings provide causal mechanistic evidence for the role of brain regions in the recovery from depression via psilocybin.
What the full text shows
Shown in the lab- Identification of specific brain regions implicated in the transition from a depressive brain state to a healthy one, which correlate with in vivo density maps of serotonin receptors 5-HT2A and 5-HT1A. (Treatment responders defined as having >50% reduction in depressive symptoms)
- Responders to psilocybin therapy were identified among the patient group. — 6 out of 15 patients (Treatment-resistant major depression; doses of 10mg and 25mg psilocybin 7 days apart)
- Setup
- 15 patients with treatment-resistant depression (fMRI data) and a structural connectivity template derived from 16 healthy young adults (DTI data).
- How
- The researchers used whole-brain modelling (Landau-Stuart oscillator) to fit pre-treatment resting-state fMRI dynamics and applied dynamic sensitivity analysis via systematic perturbation to identify regions driving state transitions. They employed Leading Eigenvector Dynamics Analysis and k-means clustering (k=3) to define Probabilistic Metastable Substates (PMS).
- Limitations
- Small sample size (15 patients) · Reliance on a group-based structural connectivity template from healthy adults rather than individual patient connectomes
Summarized by Gemma 4 from the open-access full text · 8 Oct 2026. Read it