Generative modelling of the thalamo-cortical circuit mechanisms underlying the neurophysiological effects of ketamine
Alexander D Shaw, Suresh Muthukumaraswamy, Neeraj K. Saxena, Rachael Sumner, Natalie Adams, Rosalyn J. Moran, Krish D. Singh
bioRxiv Preprint Server May 5, 2020 preprint DOI: 10.1101/688044 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Computational modeling study with Bayesian model selection |
|---|---|
| Intervention | Ketamine |
| Dose | subanaesthetic |
| Topics | Ketamine Esketamine |
| Keywords | Ketamine effects Ketamine mechanisms Computational neuroscience Brain modeling Neural modeling Brain activity Brain function Brain waves Neuropharmacology Drug mechanisms Neurotransmitters |
| Citations | 2 |
| Key points | Ketamine-induced changes in alpha and gamma oscillations are explained by increased synaptic connectivity in specific thalamo-cortical circuits involving AMPA, NMDA, and GABA-A receptors. |
Abstract
Cortical recordings of task-induced oscillations following subanaesthetic ketamine administration demonstrate alterations in amplitude, including increases at high-frequencies (gamma) and reductions at low frequencies (theta, alpha). To investigate the population-level interactions underlying these changes, we implemented a thalamo-cortical model (TCM) capable of recapitulating broadband spectral responses. Compared with an existing cortex-only 4-population model, Bayesian Model Selection preferred the TCM. The model was able to accurately and significantly recapitulate ketamine-induced reductions in alpha amplitude and increases in gamma amplitude. Parameter analysis revealed no change in receptor time-constants but significant increases in select synaptic connectivity with ketamine. Significantly increased connections included both AMPA and NMDA mediated connections from layer 2/3 superficial pyramidal cells to inhibitory interneurons and both GABAA and NMDA mediated within-population gain control of layer 5 pyramidal cells. These results support the use of extended generative models for explaining oscillatory data and provide in silico support for ketamine’s ability to alter local coupling mediated by NMDA, AMPA and GABA-A.