Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?

Abstract

Diagnosis and individualized treatment of autism spectrum disorder (ASD) represent major problems for contemporary psychiatry. Tackling these problems requires guidance by a pathophysiological theory. In this paper, we consider recent theories that re-conceptualize ASD from a "Bayesian brain" perspective, which posit that the core abnormality of ASD resides in perceptual aberrations due to a disbalance in the precision of prediction errors (sensory noise) relative to the precision of predictions (prior beliefs). This results in percepts that are dominated by sensory inputs and less guided by top-down regularization and shifts the perceptual focus to detailed aspects of the environment with difficulties in extracting meaning. While these Bayesian theories have inspired ongoing empirical studies, their clinical implications have not yet been carved out. Here, we consider how this Bayesian perspective on disease mechanisms in ASD might contribute to improving clinical care for affected individuals. Specifically, we describe a computational strategy, based on generative (e.g., hierarchical Bayesian) models of behavioral and functional neuroimaging data, for establishing diagnostic tests. These tests could provide estimates of specific cognitive processes underlying ASD and delineate pathophysiological mechanisms with concrete treatment targets. Written with a clinical audience in mind, this article outlines how the development of computational diagnostics applicable to behavioral and functional neuroimaging data in routine clinical practice could not only fundamentally alter our concept of ASD but eventually also transform the clinical management of this disorder.

Keywords: Asperger syndrome; Bayesian inference; Bayesian models; autism spectrum disorder; diagnostic tests; generative modeling; neuroimaging; translational research.

Figure 1

Schematic of the principles of a generative model.

Figure 2

Principles of Bayesian inference. (A) A prior belief (knowledge, expectation, or prediction; dotted line) is combined with the likelihood (observed data, e.g., sensory input; solid line) in the form of Gaussian probability distributions. The width of the curves represents uncertainty (variance); its inverse (the narrowness of the curve) represents the precision of or the confidence in the respective belief or data. The resulting posterior belief (dashed-dotted line) represents the updated belief, as a precision-weighted compromise between prior and likelihood, which is dominated by the quantity with higher precision. In cognition, perception can be understood as the formation of a posterior belief in response to sensory input. The lower panels show two additional situations, in which the posterior (perception) is biased toward the (sensory) data: in one case because the prior (belief) is unprecise (B); in the other, because the (sensory) data are over-precise (C).

Figure 3

Bayesian inference in the brain. (A) The “Bayesian brain” predicts (based on its internal model) the incoming sensory input from the environment and compares it with the actual input. The difference between prediction and sensory input is called prediction error. The brain’s homeostatic goal is to minimize prediction errors. Prediction errors can be reduced in two ways: action or learning. (B) Predictions can be fulfilled by choosing actions that lead to expected sensory inputs. (C) Incorrect predictions can be adapted according to prediction error. Under this model update (learning), the prediction error is explained away. (D) Due to stochasticity in the environment (1) and noise of sensory channels (2), prediction errors can usually not be explained away completely (3). Their impact on belief updates depends on the relative precision of sensory input and prediction (4), which is coded in higher levels of the internal model (5).

Figure 4

Autistic symptoms from a Bayesian viewpoint. (A) Perception. Weakly established abstract representations provide predictions with low precision and fail in guiding attention toward informative stimuli. Especially for complex stimuli with frequent irrelevant variations, such as social stimuli, attention can be attracted by unpredicted changes in irrelevant formal aspects. Overweighted low-level prediction errors, due to overly high sensory precision, cause overfitting of the internal model and difficulties in extracting meaningful information. This impairs the establishment of high-level (abstract) representations and reduces the ability to explain away future prediction errors. (B) Behavior. Minimization of prediction errors is achieved more easily by moving away from unpredictable environments into highly regular environments with repetitive actions and rituals, since they can be precisely predicted by a model without many levels of abstraction. (C) Interaction. Social interactions in particular are characterized by complex dynamic processes and irrelevant random features, which require regularization and suppression by an internal model with a high degree of abstraction and precise predictions. A relative lack of this model makes it difficult to infer the causes of social stimuli (i.e., understand the meaning of social processes) and, thus, to interact with others.