What is mood? A computational perspective

Abstract

The neurobiological understanding of mood, and by extension mood disorders, remains elusive despite decades of research implicating several neuromodulator systems. This review considers a new approach based on existing theories of functional brain organisation. The free energy principle (a.k.a. active inference), and its instantiation in the Bayesian brain, offers a complete and simple formulation of mood. It has been proposed that emotions reflect the precision of - or certainty about - the predicted sensorimotor/interoceptive consequences of action. By extending this reasoning, in a hierarchical setting, we suggest mood states act as (hyper) priors over uncertainty (i.e. emotions). Here, we consider the same computational pathology in the proprioceptive and interoceptive (behavioural and autonomic) domain in order to furnish an explanation for mood disorders. This formulation reconciles several strands of research at multiple levels of enquiry.

Fig. 1.

The figure shows how mood can be conceptualised according to the expected precision (μ) and precision of precision (τ) in a two-dimensional space. Here, precision per se corresponds to the predictability of the (prosocial, affiliative and interoceptive) world – and the two dimensions correspond to hyperpriors over precision. It is proposed that pathological changes in mood occur in the extrema of this space, as highlighted. Depression occurs when an uncertain, unpredictable outcome is predicted with high precision (red lines) resulting in a chronic, self-maintaining negative emotional state that is resistant to revision. Mania (blue lines) is characterised by an equally high precision, but with the expectation of a predictable and controllable outcome – correspondingly the environment is chronically and inappropriately labelled as such. Anxiety (green lines) is an expected unpredictability but with low precision. As such, the individual engages in behaviour designed to resolve this uncertainty but which never does. D, depression; M, Mania; Ax, anxious depression.

Fig. 2.

The figure shows a schematic of the neuromodulatory systems with the probability distributions the embody also displayed. Ascending projections (prediction errors) are shown in red and cortical projections (predictions of precision) are in blue. The expected precision (at different levels of the cortical hierarchy) is encoded by a tonic drive – that exerts a gain control over the red (ascending) projections. Each ascending projection conveys some newsworthy (unpredicted) information. The cortical hierarchy assembles this information (i.e. prediction errors) into an updated representation of the body and world – including its predictability. Our focus in this paper is predictions of predictability (i.e. precision) that are informed by the amplitude of prediction errors from different parts of the cortical hierarchy. (a) Shows the balance in a healthy system. Mood is liable to change with environmental fluctuations due to a precision that mediates fluctuations in synaptic gain. (b) Shows how this fails in depression. A chronically stressful environment has mandated a tonically depleted serotonin drive and the estimated precision is chronically low. This precludes precise prior beliefs (and adaptive stress reducing influences from, e.g prefrontal cortex), thereby exposing cortical updating to ascending (unattenuated) autonomic drives. It is important to note this schematic is highly simplified and that similar changes may play out in other neuromodulatory systems across other mood disorders.