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Uncertainty and stress: Why it causes diseases and how it is mastered by the brain p.1 (Peters et al., 2017)

#Abstract The term stress coined in 1936 has many de f nitions, but until now has lacked a theoretical foundation. Here we present an information-theoretic approach based on the free energy principle de f ning the essence of stress namely, uncertainty. We address three questions: What is uncertainty? What does it do to us? What are our resources to master it? Mathematically speaking, uncertainty is entropy or expected surprise . The free energy principle rests upon the fact that self-organizing biological agents resist a tendency to disorder and must therefore minimize the entropy of their sensory states. Applied to our everyday life, this means that we feel uncertain, when we anticipate that outcomes will turn out to be something other than expected and that we are unable to avoid surprise. As all cognitive systems strive to reduce their uncertainty about future outcomes, they face a critical constraint: Reducing uncertainty requires cerebral energy. The characteristic of the vertebrate brain to prioritize its own high energy is captured by the notion of the sel f sh brain . Accordingly, in times of uncertainty, the sel f sh brain demands extra energy from the body. If, despite all this, the brain cannot reduce uncertainty, a persistent cerebral energy crisis may develop, burdening the individual by allostatic load that contributes to systemic and brain malfunction (impaired memory, atherogenesis, diabetes and subsequent cardio- and cerebrovascular events). Based on the basic tenet that stress originates from uncertainty, we discuss the strategies our brain uses to avoid surprise and thereby resolve uncertainty. 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). p.1 (Peters et al., 2017)
On this background, stress is regarded as a form of uncertainty: We often make false predictions about the world, and our prediction errors are therefore large. When stressed, we f nd ourselves unable to avoid prediction errors or resolve uncertainty. In short, brains in distress do not work at their minimum of free energy . p.5 (Peters et al., 2017)
Thus, early life adversity and failed attachment to parental, but also any strong positive or negative experience in school, work and personal life in uence the way the Bayesian Brain selects its strategy for securing the future wellbeing. p.5 (Peters et al., 2017)

2. The Bayesian brain p.5 (Peters et al., 2017)

The aim of the current paper is to disclose those neuroendocrine mechanisms that could fulfll Bayesian functions in regulating attention, learning, and habituation. In brief, we will consider the resources the Bayesian brain has at its disposal to master uncertainty but also what happens when uncertainty cannot be resolved. p.5 (Peters et al., 2017)

2.1. Perceptual inference p.5 (Peters et al., 2017)

Furthermore, both forms of Bayesian inference can be cast as a process that minimizes variational free energy, where free energy is, effectively, the overall amount of prediction error. It has been shown that both perceptual inference and learning can be described as a minimization of free energy or the suppression of prediction errors (Friston, 2005 Rao and Ballard, 1999). p.6 (Peters et al., 2017)

2.1.1. Two modalities of Bayesian updating p.6 (Peters et al., 2017)

Clearly, the brain also has to select the news channels it should attend to. This process of selection corresponds to increasing the volume or gain of precise or reliable prediction errors. Gain modulation is a phenomenon commonly observed in neuroscience that alters the amplitude of a neuronal response, but not its selectivity. p.7 (Peters et al., 2017)
Put simply, the higher the precision of prediction errors, the greater their in uence on belief updating. During stress, neuromodulation amplifes the precision of sensory prediction errors, endowing sensory information with greater weight, in relation to prior expectations. #sectionsummary p.7 (Peters et al., 2017)

2.1.2. Precision and attention p.7 (Peters et al., 2017)

2.1.3. The anatomical correlates of hierarchical predictive coding p.7 (Peters et al., 2017)

The brain s architecture is hierarchically organized (Felleman and Van, 1991). This organization has been intensively investigated in the visual system: the lower cortical areas are located closer to primary sensory input, while the higher areas play an associational role. The hierarchical architecture enables the brain to learn its own priors as well as the intrinsic causal structure of the world that creates the sensory input. In hierarchical Bayesian inference (or Page.7: predictive coding) the priors at intermediate levels now become empirical priors . This follows because they become accountable to empirical (sensory) data and can therefore be optimized to minimize prediction errors at each hierarchal level. Neuroanatomically, the notion of a hierarchy is based on the distinction between bottom-up and top-down connections (Salin and Bullier, 1995). The notion of top-down connections provided a better explanation of experimental data than the idea that only bottom-up connections are necessary (Garrido et al., 2007). Top- down connections arise largely from layer-5-pyramidal cells and target layer-2-pyramidal cells of lower cortical areas (Fig. 4). Conversely, bottom-up connections arise largely in layer-2- pyramidal cells and project to the spiny layer-4-neurons of a higher cortical area. p.7 (Peters et al., 2017)
In summary, perceptual inference reduces our uncertainty about what caused our sensory observations. Next, we consider the important fact that we can choose which sensations to sample. #sectionsummary p.8 (Peters et al., 2017)

2.2. Active inference p.8 (Peters et al., 2017)

As avoidance behavior impairs epistemic foraging, it can promote adherence to outdated beliefs, thereby causing long-term uncertainty . In short, there is an optimum balance between approach and avoidance behavior that rests upon environmental volatility and, more importantly, the ability of an agent to estimate volatility and use it to minimize long-term uncertainty (Friston, 2009). By analogy, p.9 (Peters et al., 2017)
Recently, goal-directed-decision making has been considered in terms of active inference (Friston et al., 2013, 2014). In other words, the problem of selecting behavioral strategies can be treated as an inference problem. For such a decision-making process three kinds of probability distributions are relevant: Probability distributions over the current states of the world/body, states that can be reached, i.e., attainable states, states that the agent believes he/she should occupy, i.e., goal states. p.9 (Peters et al., 2017)
The states that agents believe they should occupy are represented in regions like the ventromedial prefrontal cortex prefrontal cortex (vmPFC) and the orbitofrontal cortex (OFC) p.9 (Peters et al., 2017)
In summary, an important aspect of resolving uncertainty is the selection of actions or strategies that reduce expected surprise, in relation to prior preferences or goals. #sectionsummary p.9 (Peters et al., 2017)

2.2.1. Goal-directed-decision making p.9 (Peters et al., 2017)

The beliefs about the current states of the world are continuously updated during perceptual inference. The lateral prefrontal cortex (PFC) is a key brain region where current environmental states are thought to be encoded (Panagiotaro- poulos et al., 2012). Thus, this brain region represents updated empirical priors or posterior beliefs about the current states of the world . The lateral PFC occupies a high hierarchical position in the brain. It sends predictions to the sensory cortex, which is located at a lower level, and in turn the lateral PFC receives prediction errors from the sensory cortex. The viscerosensory cortex evaluates interoceptive signals (e.g. pain, cutaneous light sensual touch and thermal sensations) that result from changes in the internal body milieu (viscera, muscles and skin) (Barrett and Simmons, 2015 Chanes and Barrett, 2016). p.9 (Peters et al., 2017)

2.2.2. The degree of uncertainty about what to do next p.9 (Peters et al., 2017)

With such an emergency activation of the ACC-amygdala complex, the individual experiences feelings of threat, uncertainty and lack of control. As has been shown experimentally, persons who display the largest stress responses exhibit lowest levels of self-esteem and locus of control, i.e. self-concept of own competence (Kirschbaum et al., 1995 Pruessner et al., 2005, 1999). In extreme cases, however, when it appears precluded that any of the available strategies can achieve the goal state (i.e., every strategy exhibits an extremely large KL divergence), the individual may despair and abandon his/her goal. p.10 (Peters et al., 2017)
confronted with three possible outcomes: The f rst outcome indicates good stress it represents a satisfying result certainty could be regained and the individual experiences a sense of mastery and good self-esteem wellbeing is restored completely (Fig. 1 asterisk) (McEwen and Gianaros, 2010). The second outcome speci f es tolerable stress in this case, the individual could not undo the changes in the inhospitable environment however, uncertainty could be reduced through buffering mecha- nisms such as habituation. These people show only low stress responses and intermediate levels of self-esteem and locus of control (Kirschbaum et al., 1995 Pruessner et al., 2005, 1999). This second outcome is discussed later on in the chapter Habituation updating of goal states . The third outcome characterizes toxic stress in this case, the buffering mechanisms failed, and the individuals remain trapped in the inhospitable environment their stress responses are maximal whereas their levels of self-esteem and locus of control are minimal (Kirschbaum et al., 1995 Pruessner et al., 2005, 1999) these persons are at high risk for physical and mental morbidity and mortality (Fig. 1 two asterisks) (McEwen, 2012). p.10 (Peters et al., 2017)

3.1. Attention the procurement of more precise sensory information for Bayesian updating p.10 (Peters et al., 2017)

3. Mastering uncertainty p.10 (Peters et al., 2017)

As mentioned, the brain uses of three processes to master uncertainty: attention, learning, and habituation. Crucially, this repertoire of uncertainty resolving processes is closely intertwined with cerebral and systemic energy metabolism. p.10 (Peters et al., 2017)
LC activation increases the likelihood that an action potential results in the release of glutamate, while low LC activation decreases that likelihood (Chiu et al., 2011 Kobayashi p.11 (Peters et al., 2017)
In summary, stress ignites multiple NE hotspots, thereby selectively enhancing the transmission of precise sensory information. #sectionsummary p.11 (Peters et al., 2017)
#Glucose Remarkably, an experimen- tal mental or psychosocial challenge increases whole-brain- glucose uptake by more than 10 (Hitze et al., 2010 Madsen et al., 1995). p.11 (Peters et al., 2017)

3.1.2. Energetic constrains on information transmission p.11 (Peters et al., 2017)

In summary, NE ignites hotspots at neurotransmitter release sites, thus increasing the transmitted information (bits per second). #norepinephrine p.12 (Peters et al., 2017)
Under non-stress conditions, LC-activation is low and the brain operates in an economic energy-effcient mode, abstaining from the [mis]use of its potentially higher information processing capacity. Under stress conditions, however, LC-activation is high and the brain operates in an energetically expensive mode, exploiting its full information processing capacity, while forsaking optimal energy eff ciency. p.12 (Peters et al., 2017)

In short, the use of the energetically expensive mode is restricted to times of stress and uncertainty. #sectionsummary p.12 (Peters et al., 2017)

3.1.3. Prediction errors are encoded with higher precision during stress p.12 (Peters et al., 2017)

Note: Bandwidth of information intake #TxRate In neurons with a maximal f ring rate of 400 Hz, transmission capacity is maximal, if they f re at half of their maximum rate i.e. 200 Hz. Yet in practice, the mean f ring rates of neurons in vivo is much lower around 4 Hz (Harris et al., 2012). Against this background, it seems plausible that such a low basal glutamate release probability could be enhanced on demand. p.13 (Peters et al., 2017)
#Correlation A few years ago, it was shown that if the input from an excitatory neuron A and the input from an inhibitory neuron B are correlated, then the uctuations of the postsynaptic neuron decrease (Salinas and Sejnowski, 2001). Let us assume that L2-error-unit-pyramidal cells receive inhibitory input (conveying predictions from the level above) and excitatory input (encoding the empirical priors from the current level). If predictions from the level above and priors from the current level are highly correlated, then uctuations of L2-error units will be attenuated. Conversely, if there is a mismatch, then synaptic uctuations will ensue, and the L2-error unit will start f ring. In this way, the L2-error units are capable of encoding and conveying prediction errors. p.13 (Peters et al., 2017)

3.1.4. NE increases the precision with which prediction errors induce Bayesian updating p.13 (Peters et al., 2017)

N{Error detection}