Glutamatergic Signaling Drives Ketamine-Mediated Response in Depression: Evidence from Dynamic Causal Modeling

Background: The glutamatergic modulator ketamine has rapid antidepressant effects in individuals with major depressive disorder and bipolar depression. Thus, modulating glutamatergic transmission may be critical to effectively treating depression, though the mechanisms by which this occurs are not fully understood. Methods: This double-blind, crossover, placebo-controlled study analyzed data from 18 drug-free major depressive disorder subjects and 18 heathy controls who received a single i.v. infusion of ketamine hydrochloride (0.5 mg/kg) as well as an i.v. saline placebo. Magnetoencephalographic recordings were collected prior to the first infusion and 6 to 9 hours after both ketamine and placebo infusions. During scanning, participants passively received tactile stimulation to the right index finger. Antidepressant response was assessed across timepoints using the Montgomery-Asberg Depression Rating Scale. Dynamic causal modeling was used to measure changes in alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA)- and N-methyl-D-aspartate (NMDA)-mediated connectivity estimates in major depressive disorder subjects and controls using a simple model of somatosensory evoked responses. Results: Both major depressive disorder and healthy subjects showed ketamine-mediated NMDA-blockade sensitization, with major depressive disorder subjects showing enhanced NMDA connectivity estimates in backward connections and controls showing enhanced NMDA connectivity estimates in forward connections in our model. Within our major depressive disorder subject group, ketamine efficacy, as measured by improved mood ratings, correlated with reduced NMDA and AMPA connectivity estimates in discrete extrinsic connections within the somatosensory cortical network. Conclusions: These findings suggest that AMPA- and NMDA-mediated glutamatergic signaling play a key role in antidepressant response to ketamine and, further, that dynamic causal modeling is a powerful tool for modeling AMPA- and NMDA-mediated connectivity in vivo.