CNS*2026 Halifax

Introduction
The excitation-inhibition (E:I) ratio is a key biomarker in psychiatric conditions, and can be modulated by pharmacological interventions. Ketamine, an NMDA receptor antagonist, blocks NMDA receptors on inhibitory neurons, driving cortical disinhibition. Electrophysiologically, ketamine reduces the mismatch negativity (MMN) signal, which is a measure of sensory surprise within the brain's predictive coding framework [1]. Connectome-based neural-mass models excel at linking macroscopic electrophysiology to microcircuit mechanisms. Here, we extend a conductance-based neural mass model into a whole-brain framework to validate its capacity to capture ketamine's specific effects on NMDA receptor dynamics.


Methods
We modeled a previously published EEG dataset from 19 subjects recorded during a roving auditory MMN task under placebo and ketamine conditions [2] using a whole-brain modeling framework. We parcellated the brain into 200 distinct regions using Schaefer atlas. We developed an extension of the conductance-based neural-mass model introduced in [3] to simulate voltage (v) and gating (g) for AMPA, GABA, and NMDA receptors across pyramidal, excitatory, and inhibitory populations in the parcellated regions. We computed normalized gating by voltage interactions and applied principal component analysis (PCA) across different conditions to isolate and compare dominant temporal trajectories between pharmacological interventions.


Results
Analysis of the primary temporal trajectories (PC1) revealed distinct activation profiles across AMPA, GABA, and NMDA receptors following stimulus onset. Under placebo conditions, the network exhibited a robust MMN response. This was particularly evident in the normalized integrated synaptic activity (NMDA+AMPA+GABA), which produced a prominent deflection in both excitatory and pyramidal populations. Administration of ketamine markedly attenuated this effect across these key populations. Decomposing these network-level changes by receptor type demonstrated that ketamine primarily blunted the differential activation within GABA and NMDA signaling pathways.


Discussion

References

1. Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009). The mismatch negativity: A review of underlying mechanisms. Clinical Neurophysiology, 120(3), 453–463. https://doi.org/10.1016/j.clinph.2008.11.029 
   
2. Schmidt A, Bachmann R, Kometer M, Csomor PA, Stephan KE, Seifritz E, & Vollenweider FX. (2012). Mismatch negativity encoding of prediction errors predicts S-ketamine-induced cognitive impairments. Neuropsychopharmacology, 37(4), 865–875. https://doi.org/10.1038/npp.2011.261 
   
3. Marreiros, A. C., Kiebel, S. J., Daunizeau, J., Harrison, L. M., & Friston, K. J. (2009). Population dynamics under the Laplace assumption. NeuroImage, 44(3), 701–714. https://doi.org/10.1016/j.neuroimage.2008.10.008 
   

Acknowledgement
We acknowledge the support of Canadian Institute of Health Research (CIHR-Project Grant) and Swiss Neuromatrix Foundation.