Reshaping computational neuropsychiatry beyond synaptopathy.

Abstract

Computational neuropsychiatry is a leading discipline to explain psychopathology in terms of neuronal message passing, distributed processing, and belief propagation in neuronal networks. Active Inference (AI) has been one of the ways of representing this dysfunctional signal processing. It involves that all neuronal processing and action selection can be explained by maximizing Bayesian model evidence, or minimizing variational free energy. Following these principles, it has been suggest that dysconnection in neuronal network results in aberrant belief updating and erroneous inference, leading to psychiatric and neurologic symptoms. However, there is a classic distinction between disorders of inference (or synaptopathy - including the majority of psychiatric disorders), and disorders of brain function (including vascular neurological pathologies and severe forms of tauopathy and synucleinopathies). This distinction is generally based on the idea that synaptopathies impair neuromodulatory precision weighting, leading to rigid inferences or heightened sensitivity to noise, while disorders of brain function are linked to damage in the nervous system (disconnection). This makes it challenging to apply the logic of the free energy principle. We suggest that this distinction will enable future models of neuropsychiatric symptoms to be improved by considering more than neuronal message passing.