Bayesian Inference, Dysconnectivity and Neuromodulation in Schizophrenia

This scientific commentary refers to ‘Estimating changing contexts in schizophrenia’, by Kaplan et al. (doi:10.1093/brain/aww095). The paper by Kaplan et al. in this issue of Brain addresses one of the most interesting questions in contemporary schizophrenia research: the role of uncertainty during perception (Kaplan et al. , 2016). Uncertainty enjoys much interest in schizophrenia research as it may provide a crucial link between core clinical symptoms of schizophrenia—aberrant perceptual inference (e.g. hallucinations) and abnormal beliefs (delusions)—and long-standing neurobiological findings that patients with schizophrenia display widespread alterations in structural and functional brain connectivity (dysconnectivity). These two cardinal features of schizophrenia have been integrated in disease theories, which have developed in three waves. A first influential proposal was that dysconnectivity in schizophrenia arises from abnormal regulation of NMDA receptor (NMDAR)-dependent transmission by neuromodulatory (dopaminergic and cholinergic) influences (Friston, 1998). Given the critical role of NMDARs for synaptic plasticity and myelination, this suggested that both neurodevelopmental aspects of schizophrenia ( cf . abnormal pruning of connections by altered experience-dependent plasticity) and structural dysconnectivity might arise from a primary disturbance of NMDAR-dependent plasticity due to aberrant neuromodulatory control. Second, these putatively abnormal NMDAR-neuromodulator interactions (NNI) were proposed to cause a central computational impairment in schizophrenia: abnormal hierarchical Bayesian inference in the cortex (Stephan et al. , 2006). This proposal was inspired by the notion that the brain constructs a hierarchical and probabilistic model of the world in order to infer the environmental causes of its sensory inputs (predictive coding), and by the increasingly discernible importance of NMDAR-neuromodulator interactions for implementing hierarchical Bayesian inference in the brain (Fig. 1). Under generic conditions, belief updates in Bayesian inference are driven by prediction errors (the difference between actual and predicted inputs) but, critically, weighted by how uncertain or precise both predictions and sensory inputs are. While …