Perturbing whole-brain models of brain hierarchy: An application for depression following pharmacological treatment

Abstract

Determining the scale of neural representations is a central challenge in neuroscience. While localized representations have traditionally dominated, evidence suggests information is also encoded in distributed, hierarchical networks. Recent research indicates that the hierarchy of causal influences shaping functional patterns serves as a signature of distinct brain states, with implications for neuropsychiatric disorders. Here, we first explore how whole-brain models, guided by the thermodynamics of mind framework, estimate brain hierarchy and how perturbing such models enables the study of in-silico transitions represented by static functional connectivity. We then apply this to major depressive disorder, where different brain hierarchical reconfigurations emerge following psilocybin and escitalopram treatments. We build resting-state whole-brain models of depressed patients before and after interventions and conduct a dynamic sensitivity analysis to explore brain states’ susceptibility—measuring their capacity to change—and their drivability to healthier states. We show that susceptibility is on average reduced by escitalopram and increased by psilocybin, and that both treatments promote healthier transitions. These results align with the post-treatment window of plasticity opened by serotonergic psychedelics and the similar clinical efficacy of both drugs in trials. Overall, this work demonstrates how whole-brain models of brain hierarchy can inform in-silico neurostimulation protocols for neuropsychiatric disorders.