Ketamine’s Mechanisms of Action in Major Depressive Disorder and Treatment-Resistant Depression: Is Brain Topological Change a Candidate Convergent Systems-Level Pathway?

Ketamine is a rapid-acting antidepressant with demonstrated efficacy in major depressive disorder and treatment-resistant depression, yet the mechanisms by which its molecular actions translate into sustained clinical improvement remain incompletely understood. Prevailing models have emphasized N-methyl-D-aspartate receptor antagonism and downstream glutamatergic signaling; however, convergent evidence indicates that ketamine engages a broader hierarchy of biological processes spanning synaptic plasticity, circuit dynamics, and large-scale brain network organization. This narrative review integrates molecular, cellular, circuit-level, neuroimaging, and electrophysiological findings to evaluate whether normalization of pathological brain topology represents a convergent systems-level pathway mediating ketamine’s antidepressant effects. Across modalities, depressive disorders are characterized by disrupted network integration and segregation, stability of maladaptive configurations, and impaired flexibility of large-scale neural systems. Evidence from functional magnetic resonance imaging and electroencephalography indicates that ketamine rapidly modulates these abnormalities, increasing global connectivity, reducing rigid network organization, and enhancing neural signal diversity and network dynamics. These effects emerge within hours, are associated with clinical improvement, and show temporal and mechanistic distinctions from those observed with monoaminergic antidepressants, neuromodulation, psychotherapy, and serotonergic psychedelics. Integrating these findings, this review proposes that ketamine’s molecular and circuit-level actions are necessary but not sufficient, with antidepressant efficacy shaped by how these processes are instantiated within large-scale network topology. Conceptualizing brain topology as an integrative systems-level framework may help reconcile diverse mechanistic observations, clarify sources of treatment resistance, and inform testable hypotheses for topology-based biomarkers and novel rapid-acting antidepressant strategies.