This pre-print study uses high density EEG, Bayesian modeling, and machine learning to show that predictions (measured as reaction times) depend on alpha activity in higher order cortex (brain), beta feedback, and NMDA receptors. Ketamine blocks access to learned predictive information (i.e. also negative predictive models underlying depression).
“Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior; e.g., hearing a siren, we expect to see an ambulance and quickly make way. While there are theoretical and computational frameworks for prediction, the circuit and receptor level mechanisms are unclear. Using high density EEG, Bayesian modeling and machine learning, we show that inferred “causal” relationships between stimuli and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audio visual delayed match-to-sample task which elicited predictions. Predictive beta feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, a NMDA receptor blocker but not the control drug dexmedetomidine perturbed behavioral indices of predictions, their representation in higher order cortex, feedback to posterior cortex and pre activation of sensory templates in higher order sensory cortex. This study suggests predictions depend on alpha activity in higher order cortex, beta feedback and NMDA receptors, and ketamine blocks access to learned predictive information.“
Authors: Sounak Mohanta, Mohsen Afrasiabi, Cameron Casey, Sean Tanabe, Michelle J. Redinbaugh, Niranjan A. Kambi, Jessica M. Phillips, Daniel Polyakov, William Filbey, Joseph L. Austerweil, Robert D. Sanders & Yuri B. Saalmann