Brain network modeling with The Virtual Brain derives pharmacodynamics of ketamine

Abstract

Ketamine, an N-Methyl-D-aspartate receptor (NMDAR) antagonist, is used clinically as an anesthetic and antidepressant, and is also known for its psychotomimetic effects. Its impact on brain dynamics and behavior varies significantly with dosage likely via a dose-dependent modulation of the NMDARergic transmission. Currently, it is unclear how molecular changes at the microscopic level of NMDAR antagonism lead to large-scale changes in brain dynamics. We implement a dose-dependent NMDAR antagonism based on ketamine’s disinhibition theory into a biophysically grounded mean-field model within The Virtual Brain (TVB) framework to replicate ketamine’s key signatures across its dose spectrum. Our results imply that in low doses ketamine preferentially impairs excito-inhibitory neurotransmission while in higher doses antagonism on excito-excitatory connections plays a role. These findings highlight the utility of computational modeling for disentangling dose-specific mechanisms of action and provide a framework for exploring NMDAR-related interventions. Author summary: Ketamine is a dissociative anesthetic at high doses, but at lower, sub-anesthetic doses, it has garnered significant interest for its rapid-acting antidepressant and anxiolytic effects. Despite its growing clinical use in psychiatric conditions, the precise neural mechanisms underlying ketamine’s dose-dependent effects remain incompletely understood. Ketamine primarily acts as a non-competitive antagonist of the NMDAR, which is expressed on both excitatory and inhibitory neurons throughout the cortex. One of the leading hypotheses explaining its antidepressant effects is the disinhibition theory which proposes that low doses of ketamine preferentially block NMDARs on inhibitory interneurons, resulting in increased cortical excitability. At high doses ketamine exerts anesthetic effects potentially through more widespread NMDAR antagonism including on excitatory neurons. In this study, we used a computational model to explore how selective NMDAR antagonism at different doses affects large-scale brain dynamics. A key novelty of our work is the integration of ketamine’s full dose spectrum within a single computational modeling framework, allowing us to relate distinct neural effects from disinhibition to anesthesia to experimental findings. This modeling approach contributes to a deeper understanding of how ketamine modulates cortical activity across different contexts.