Brain dynamics predictive of response to psilocybin for treatment-resistant depression

Introduction

Vohryzek and colleagues frame their study around a broader challenge in systems neuroscience: although depression is associated with altered brain dynamics, it remains difficult to identify which dynamic features are most relevant to symptoms and treatment response. The paper notes that major depressive disorder is often linked to rigid, self-referential thinking and to abnormalities in resting-state organisation, including default mode network overactivity and reduced control network function. At the same time, prior work has suggested that psychedelic therapy may increase the flexibility and repertoire of brain states, but the mechanisms underlying clinical response remain uncertain. The authors therefore aim to use empirical fMRI data and large-scale computational brain modelling to identify brain dynamics predictive of response to psilocybin in treatment-resistant depression. More specifically, they ask which spatio-temporal brain substates distinguish responders from non-responders, whether a model fitted to pre-treatment data can reproduce these dynamics, and which brain regions appear most important for driving a transition towards a healthier post-treatment state. They also hypothesise that regions involved in successful transition will relate to the distribution of 5-HT2a and 5-HT1a serotonin receptors. The paper presents an exploratory modelling analysis built on data from a clinical psilocybin study and uses this as a test case for dynamic sensitivity analysis, with the broader implication that such models might eventually support mechanistic and personalised psychiatry.