S0033291718000430jrv 2277..2284

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. The current predicament Mood disorders are heterogeneous and complex and depend upon the interplay of several neuromodulator systems and genetic and epigenetic factors (Hirschfeld, 2000; Holsboer, 2000; Nestler & Carlezon, 2006; Nutt et al. 2007; Dowlati et al. 2010; Möhler, 2012). As a result, current approaches to diagnosis and classification of mood disorders suffer several shortcomings (Nesse & Stein, 2012), and advances in understanding the underlying neurobiology have been slow. We argue that progress may be facilitated by an appreciation of the dynamic and selforganising nature of neurobiological systems (Seth, 2013; Fotopoulou, 2015; Clark et al. 2016). The brain is a generative organ Traditional hypotheses propose that the neurobiological underpinnings of a variety of disorders arise from structural or functional abnormalities in the brain consequent on a combination of environmental stress and genetic vulnerabilities (Videbech & Ravnkilde, 2004; Ota & Duman, 2013; Duman, 2014). Similarly, it has been argued that pharmacotherapy may work via its effects on neurogenesis and synaptic plasticity (Santarelli et al. 2003; Anacker et al. 2011; Castrén & Hen, 2013) and that stressors may leave an indelible scar on key neurobiological systems that disrupts their structure and/or function. In this sense, the traditional hypothesis is that brain abnormalities develop throughout the lifespan and result in the onset and maintenance of psychopathology. However, modern accounts of brain function reject the classical notion of the brain as a passive organ in favour of theories of embodiment and self-organisation (Friston, 2010; Seth, 2013; Corlett & Fletcher, 2014). Specifically, recent theoretical accounts propose that the brain works towards an equilibrium in which its environment is rendered predictable; i.e. surprise is minimised and uncertainty resolved (Friston, 2009). Essentially, the brain embodies a generative model that encodes prior beliefs about sensory input and their causes. This model generates predictions which are tested against actual input to produce prediction errors (surprise). These prediction errors are then used by the brain to revise its model of the world. In so doing, it updates its predictions and minimises prediction error (Friston, 2010). On this view, prior beliefs about the world, as discussed above, are represented in terms of probability distributions. The sufficient statistics for these distributions may be labelled as ‘expectation’ and ‘precision’ and, if the brain embodies such priors, it follows that they must be represented in its physical activity and anatomy. It is thought that expectations (and subsequent predictions) are encoded by synaptic activity, while precision, or uncertainty, is encoded by the extent to which this activity is attenuated or amplified (Friston & Kiebel, 2009), e.g. through careful synaptic efficacy or gain control. In current predictive coding formulations of the Bayesian brain, expectation and prediction error units are thought to occupy deep and superficial pyramidal layers of cortex, respectively (Friston & Kiebel, 2009; Bastos et al. 2012; Shipp et al. 2013); such that predictions generated, from expectations, at one level of the cortical hierarchy descend to form prediction errors in superficial layers of the level below. These prediction error units then send ascending signals to update expectations. https://doi.org/10.1017/S0033291718000430 Published online by Cambridge University Press Crucially, the precision or confidence placed in prediction errors is then associated with the synaptic gain or efficacy of superficial pyramidal cells – that itself depends upon interactions with inhibitory interneurons and modulatory neurotransmission. This is a brief description of hierarchical predictive coding. In what follows, we look more closely at the back story to predictive coding; namely, free energy minimisation and allostasis. The free energy principle The brain, like other biological systems, seeks to maintain its physiological (and psychological) state in the face of a constantly changing internal and external environment and must therefore minimise entropy over external states (where entropy is a mathematical measure of uncertainty or expected surprise). Directly computing surprise is intractable, but by appealing to variational principles, we can calculate an upper boundary on surprise, namely free energy, which systems will (or will appear to) minimise (Friston et al. 2006). Given that surprise is the inverse of model evidence, if the brain is minimising a free energy bound on surprise, it is necessarily trying to maximise the sensory evidence for its model of the world and is inherently self-evidencing (Hohwy, 2016). Under some simplifying assumptions, one can equate surprise with (precision weighted) prediction error. In brief, the brain can minimise (precision weighted) prediction error in three ways. First, expectations can be updated (by changing neuronal activity) so that predictions provide a better explanation for sensory inputs (Friston et al. 2006). Alternatively, the brain can change the world or the way it is sampled (by engaging motor and autonomic reflexes) so that sensations fall into line with predictions. This provides a simple explanation for behaviour, which becomes the fulfilment of predicted (proprioceptive and somatosensory) motor sensations. Finally, both of these processes (perception and action) can be nuanced by optimising the precision of prediction errors. In cognitive neuroscience, this optimisation has been framed in terms of attention and attenuation. In other words, attending to a sensory stream corresponds to increasing its sensory precision through appropriate synaptic gain control. Conversely, sensory attenuation corresponds to the reduction of precision by attending away from or ignoring the consequences of one’s own action. The active minimisation of free energy is known as active inference, which means the brain can selectively sample from data that concur with its current expectations (Friston et al. 2011; Pezzulo, 2012). In the interoceptive domain, the resolution of prediction errors through autonomic reflexes provides a simple account of homoeostasis. This formulation can be extended by appealing to hierarchical generative models such that predictions at higher levels pre-empt the need for homoeostasis [e.g. in a hypoglycaemic state, by attenuating the precision of prediction errors reporting hypoglycaemia, we can suspend the reflex mobilisation of glucose and act on the world (by eating) to fulfil and maintain higher level predictions] (Pezzulo et al. 2015). This hierarchical minimisation of prediction errors allows allostatic control over homoeostatic reflexes (Sterling & Eyer, 1988; McEwen, 1998; Ramsay & Woods, 2014). Note that the balance between allostasis and homoeostasis depends on attenuating interoceptive prediction errors, which will be an important theme in what follows. In this way, a self-regulating embodied loop of circular causality is constructed in which the brain constructs the external environment (and internal milieu) it expects to encounter, which in turn reinforces its predictions (Seth, 2014; Barrett & Simmons, 2015). Free energy and emotion Recent theoretical arguments have converged on the idea that emotional states reflect changes in the uncertainty about the somatic consequences of action (Joffily & Coricelli, 2013; Wager et al. 2015; Seth & Friston, 2016). This uncertainty refers to the precision with which motor and physiological states can be predicted. In this setting, negative emotions contextualise events that induce expectations of unpredictability, while positive emotions refer to events that resolve uncertainty and confer a feeling of control (Barrett & Satpute, 2013; Gu et al. 2013). This ties emotional states to the resolution of uncertainty and, through the biophysical encoding of precision, to neuromodulation and cortical gain control (Brown & Friston, 2012). In summary, one can associate the valence of emotional stimuli with the precision of prior beliefs about the consequences of action. In this view, positively valenced brain states are necessarily associated with increases in the precision of predictions about the (controllable) future – or, more simply, predictable consequences of motor or autonomic behaviour. Conversely, negative emotions correspond to a loss of prior precision and a sense of helplessness and uncertainty about the consequences of action.