Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

Abstract

The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, predictive coding views perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs). Disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia. Here, we provide empirical evidence for this clinical theory, demonstrating the existence of multiple, hierarchically related PEs in a "roving MMN" paradigm.We applied a computational model (Hierarchical Gaussian Filter, HGF), to single-trial EEG data from healthy volunteers that received the NMDAR antagonist S-ketamine in a placebocontrolled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our computational trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207ms post-stimulus), while high-level PEs (about transition probability) are reflected by later components (152-199ms, 215-277ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts inference on abstract statistical regularities.Our findings suggest that NMDAR dysfunction impairs hierarchical Bayesian inference about the world's statistical structure. Beyond the relevance of this finding for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential disease mechanisms.

State anxiety biases estimates of uncertainty during reward learning in volatile environments

“…Trial-wise estimates of uncertainty -or its inverse, precision -serve to scale the impact of prediction errors (PEs) on the belief updates. Consistent with previous reports (Stefanics et al, 2018;Weber et al, 2019), we found that precision-weighted PEs (pwPEs) on two hierarchical levels can explain trial-wise modulation of observed ERP responses in control participants.…”

“…Lower level pwPEs about the reward outcomes, updating beliefs about reward tendency and higher-level pwPEs about reward tendencies, updating volatility estimates, were primarily represented across frontocentral regions, with an earlier latency for ε3 (387 ms) than ε2 (453 ms). These results align with previous studies combining EEG analyses with the HGF, which revealed that multiple, hierarchically-related pwPEs are computed while learning in volatile environments; and these are represented across different brain regions specific to the task demands (Diaconescu et al, 2017a;Stefanics et al, 2018;Weber et al, 2019).…”

“…We used the Hierarchical Gaussian Filter (HGF) from Mathys et al (2014Mathys et al ( , 2011 to estimate each participant's individual learning characteristics and belief trajectories during our binary reward-learning task. The HGF has been applied to understand learning across diverse settings Diaconescu et al, 2017bDiaconescu et al, , 2014Iglesias et al, 2013;Marshall et al, 2016;Stefanics et al, 2018;Weber et al, 2019). It is implemented in the freely available open source software TAPAS (http://www.translationalneuromodeling.org/tapas).…”

“…More compelling evidence linking PEs, Bayesian surprise, and belief updating to changes in ERP responses comes from studies combining computational modelling and analysis of trialwise EEG responses (Diaconescu et al, 2017a;Jepma et al, 2016;Kolossa et al, 2015;Mars et al, 2008;Stefanics et al, 2018;Weber et al, 2019). For instance, recent EEG studies on the MMN were able to spatiotemporally dissociate lower-level precision-weighted PE (pwPE) signals, which drive updates in belief estimates (Stefanics et al, 2018), and higher-level pwPEs, driving volatility updates (Weber et al, 2019). In addition, model-based single-trial analyses of the P300 identified the earlier P3a waveform of anterior distribution as an index of belief updating, whereas Bayesian surprise was represented in the later posterior P3b component (Kolossa et al, 2015).…”

“…The HGF estimates individual trajectories of trial-wise belief updates governed by hierarchically related PEs based on the responses of participants. To reveal the effect of hierarchical PEs and precision weights on evoked brain responses, we used the relevant hierarchical computational quantities (pwPEs) as regressors in a general linear model (GLM) of trial-wise EEG amplitudes as done in previous studies (Diaconescu et al, 2017a(Diaconescu et al, , 2014Weber et al, 2019).…”