Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Abstract

Research in clinical neuroscience is founded on the idea that a better understanding of brain (dys)function will improve our ability to diagnose and treat neurological and psychiatric disorders. In recent years, neuroscience has converged on the notion that the brain is a 'prediction machine,' in that it actively predicts the sensory input that it will receive if one or another course of action is chosen. These predictions are used to select actions that will (most often, and in the long run) maintain the body within the narrow range of physiological states consistent with survival. This insight has given rise to an area of clinical computational neuroscience research that focuses on characterizing neural circuit architectures that can accomplish these predictive functions, and on how the associated processes may break down or become aberrant within clinical conditions. Here, we provide a brief review of examples of recent work on the application of predictive processing models of brain function to study clinical (psychiatric) disorders, with the aim of highlighting current directions and their potential clinical utility. We offer examples of recent conceptual models, formal mathematical models, and applications of such models in empirical research in clinical populations, with a focus on making this material accessible to clinicians without expertise in computational neuroscience. In doing so, we aim to highlight the potential insights and opportunities that understanding the brain as a prediction machine may offer to clinical research and practice.

Do eye movement abnormalities in schizophrenia cause Praecox Gefühl?

“…Combining a data‐driven approach and a theory‐driven approach may uncover the biological basis of psychiatric disorders. Among the theory‐driven approaches, the unconscious predictive processing theory has provided deeper mechanistic insights into the perceptual and cognitive deficits in several psychiatric conditions 4 . In this regard, eye movement is the very first window of unconscious predictive processing.…”

Psychiatric disorders as failures in the prediction machine

“…In this issue, Smith et al 2 . provide a review of the recent advances in the application of the theory‐driven approach in psychiatry and clinical neuroscience research.…”

“…Smith et al 2 . provide a comprehensive review of recent advances in predictive processing theory, in which, by introducing the concept of ‘interoceptive prediction,’ predictive processing theory has been extended to interoceptive systems.…”

“…Thanks to this extension of the theory, affective and somatic symptom disorders have fallen within the scope of predictive processing theory. For example, in one of the studies introduced in Smith et al ., 2 Stephan et al 4 . propose a mathematical model of homeostatic and allostatic regulation of visceral states as a hierarchical interoceptive predictive process in which homeostatic adjustment of physiological variables, such as blood osmolality and circulating hormones, can be achieved by a top‐down interoceptive prediction (allostasis) and PE (deviations from the homeostatic range) minimization process.…”

“…Using this model, they hypothesized that when attempts at higher‐level allostasis repeatedly fail, subjective fatigue and depressive behavior could emerge as strategies for dealing with conditions in which the brain predicts that allostatic regulation will be ineffective. In addition, Smith et al 2 . emphasize the importance of mathematical formulation in the sense that, by using quantitative prediction, the model makes it possible to investigate which regions of the brain show patterns of activity consistent with those simulated dynamics of prediction and PE, and identify the differences in individuals, including diagnosis and prognostic information.…”