The scale at which the brain represents information remains a key question in neuroscience. Evidence shows that information is encoded not just in localized areas but across distributed, hierarchical networks. The hierarchy of causal influences shaping brain activity patterns is a signature of different brain states, relevant to neuropsychiatric disorders. Using whole-brain models guided by the thermodynamics of mind framework, researchers estimated brain hierarchy and studied in-silico transitions in static functional connectivity. Applying this to major depressive disorder, they built resting-state whole-brain models of depressed patients before and after treatment with psilocybin or escitalopram.
Using whole-brain models guided by the Thermodynamics of Mind framework, the authors estimated the brain hierarchy of specific brain states and simulated transitions between states. Applying this to major depressive disorder, they built models of depressed patients before and after psilocybin and escitalopram treatments. Dynamic sensitivity analysis showed that susceptibility to change was on average reduced by escitalopram and increased by psilocybin, and both treatments promoted healthier transitions. These results align with the post-treatment plasticity window opened by serotonergic psychedelics and with the similar clinical efficacy of both drugs observed in clinical trials.