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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} #norepinephrine The premise of predictive coding is that precision is encoded by the response amplitude or gain of the neurons encoding prediction errors. The key point here is that the noradrenergic modulation affects presynaptic gain control and therefore contributes encoding of precision: NE increases the probability of presynaptic neurotrans- mitter release onto L2-neurons, and in so doing renders the postsynaptic L2-neuron more responsive to non-correlated inputs. Thus, NE increases the number of action potentials that the L2- error unit generates when it receives non-correlated input signals. Such an increase in the number of action potentials of the L2 error unit forwards more weight to the ascending prediction error i.e., to the bottom-up sensory information ow. p.13 (Peters et al., 2017)
#norepinephrine In summary, NE enhances information transmission at the synapses, and in so doing enables the L2-error-unit-pyramidal populations to endow prediction errors with higher precision. In this way, NE acting on the sensory cortex that occurs in situations of uncertainty adds value and weight to the bottom-up-information ow. Therefore, during stress, the sensory evidence becomes more in uential than (relatively imprecise) prior expectations. Such selective increases in precision are key for updating beliefs about the world that engender uncertainty and stress. p.13 (Peters et al., 2017)

3.1.5. The selfsh brain provides the energy for increasing precision p.13 (Peters et al., 2017)

In conclusion: During arousal, noradrenergic regulation of presynaptic neurotransmitter release probabilities leads to precise, high gain transmission of sensory evidence required to update deep, hierarchical beliefs about changes in the external and internal milieu. At the same time, the selfsh brain supplies itself with the extra energy required for precision-engineered belief updating. p.13 (Peters et al., 2017)
The second key process for mastering uncertainty is learning. Glucocorticoids control functional and structural plasticity in many brain regions (Fig. 3). p.14 (Peters et al., 2017)

3.2.1. Glucocorticoids gate the time window for cortical plasticity p.14 (Peters et al., 2017)

#LTP Here, we focus on intracellular MRs and GRs. These two intracellular receptors differ in their af f nity for cortisol: MR binds cortisol with high af f nity, GR with low af f nity (Arriza et al., 1988). Once cortisol has activated these receptors in the cytosol, MR and GR enter the cell nucleus where they exert differential effects on gene expression (de Kloet et al.,1998). Fig. 7B shows the MR and GR binding characteristics of glucocorticoids in a pyramidal cell. With respect to synaptic plasticity i.e., long-term potentiation (LTP) and long-term depression (LTD), MR and GR have been shown to act in an opposing manner on gene expression (Diamond et al., 1992 Pavlides et al., 1994, 1996). Here, we focus on how declarative memories are formed under stress, which is distinct from the way emotional memories are conserved (Maggio and Segal, 2012 Quirarte et al., 1997 Zhou et al., 2010). At low cortisol concentrations, the facilitatory effect of MRs on plasticity prevails, while at high cortisol concentrations, the inhibitory effect of GRs prevails. Due to the opposing actions of MR and GR, the difference in effects on gene expression becomes paramount (Datson et al., 2001). p.14 (Peters et al., 2017)
#Cortisol #memory Consolidation of declarative memories is most likely when glucocorticoid concentrations are low in the normal range (where the bell-shaped curve has its peak). In contrast, consolidation is unlikely when glucocorticoids are absent (left-hand side of the bell-shaped curve) (Wagner et al., 2005) or when glucocorticoid concentrations are high (right-hand side of the bell-shaped curve) (Plihal et al., 1999). Likewise, retrieval of memories also shows a bell-shaped dependency on glucocorticoids, i.e. retrieval is optimal at low (normal range) glucocorticoid concentrations, but impaired at very low and very high glucocorticoid concentrations (Rimmele et al., 2013). p.14 (Peters et al., 2017)
Note: Modulate stress response to improve learning even in situations where it would otherwise be impaired. Uncertainty is always present, whether we recognize it or not, so we must be able to learn even amidst uncertainty. Therefore, the stress response must be modulated in the face of adversity & uncertainty Clearly, if we experience the world as uncertain or ambiguous, we want to suspend learning. Conversely, if we experience it as predictable and lucid, we want to consolidate what we have learned. Given that LTP is selectively enabled over a carefully controlled window of MR-GR difference (Fig. 7), glucocorticoids open and close the time windows for learning (Joels, 2006 McEwen, 2015). They are therefore in a key strategic position to selectively consolidate when and what we learn. p.14 (Peters et al., 2017)
i{In conclusion: High glucocorticoid concentrations create a phase of change , revising the current model of the world (including its strategies). Low glucocorticoid concentrations create a phase of conservation , stabilizing the current model of the world. } p.14 (Peters et al., 2017)
Consequently, TrkB constitutes a link that mediates the bell-shaped-dose-response Page.15: dependency of LTP probability on glucocorticoids and deter- mines when changes to our internal models should or can be consolidated. Many other biological functions also show a bell-shaped dependency on glucocorticoids (de Kloet et al., 1998). In the current paper, we regard this bell-shaped dependency on glucocorticoids as embodying a reference point for the optimal level of experience-dependent plasticity. p.14 (Peters et al., 2017)
In conclusion, the probability that a synapse undergoes LTP shows a bell-shaped dependency on the glucocorticoid concentration. There is an optimal glucocorticoid concentration that favors LTP. This optimum re ects the absence of prediction errors, or in other words, the free energy minimum. p.16 (Peters et al., 2017)

3.2.2. Stress and surprisal p.16 (Peters et al., 2017)

In conclusion, if the internal model of the world makes false predictions, high glucocorticoid values indicate a high free energy (i.e., uncertainty) the model is changed and functional plasticity is precluded. Once the updated model succeeds in making correct predictions, low glucocorticoid values indicate a low free energy (i.e., predictability) the model is consolidated and functional plasticity re- emerges. p.17 (Peters et al., 2017)

3.2.3. Functional plasticity at the apical tuft p.17 (Peters et al., 2017)

i{The consolidation of a generative model through LTP or LTD occurring at the apical tuft of pyramidal neurons is selectively enabled when the world is learnable i.e. when expected surprise, uncertainty and glucocorticoid levels are low. In what follows, we consider this learning in more detail. } p.17 (Peters et al., 2017)

3.2.4. Glucocorticoids gate learning of prediction-error precision p.17 (Peters et al., 2017)

The key point here is that precision can belearned on the basis of past experiences that enable the brain topredict when sensory input will be precise or imprecise. p.17 (Peters et al., 2017)
In thisway, NE effectively increases the precision of the reportedprediction error, which manifests itself as increased attention p.17 (Peters et al., 2017)
Therefore, high glucocorticoid concentrations may lead to unlearning of the precision of prediction errors, while low concentrations of glucocorticoids may facilitate the learning of precision weighting (Liston et al., 2013 Liston and Gan, 2011). This may sound complicated however, the brain has to (i) optimize the precision of ascending prediction errors (e.g., through creating NE hotspots), it has to (ii) learn the right predictions (e.g., through synaptic plasticity that is selectively consolidated during low (normal) levels of glucocorticoid) and, f nally, it has to (iii) learn how to optimize the precision of prediction errors (e.g., through the interaction between NE and glucocorticoid levels described above). p.17 (Peters et al., 2017)
In conclusion, glucocorticoids govern how the brain learns the precision of prediction errors. Such a learning process allows us to discriminate between trustworthy and imprecise sources of information. p.17 (Peters et al., 2017)

3.2.5. Glucocorticoids gate learning of the generative model p.17 (Peters et al., 2017)

In summary, glucocorticoids also govern expectations generating predictions. A benefcial effect of glucocorticoids is that they enable us to learn how to make optimal predictions in a particular situation. p.18 (Peters et al., 2017)

3.2.6. Structural plasticity at the apical tuft p.18 (Peters et al., 2017)

#stress #plasticity High glucocorticoid concentrations have been shown to favor postsynaptic dendritic spine formation (GRs exert trophic effects via TrkB signaling) (Ikeda et al., 2015 Jeanneteau et al., 2008), whereas low glucocorticoid concentrations are required for the stabilization of freshly formed spines, the latter process being essential for memory consolidation (Liston et al., 2013). p.18 (Peters et al., 2017)
n{Chronic stress leads to world rebuilding} Thus, chronic stress results in functional and structural alterations, which can be regarded as the deconstruction of the internal representation of the world that was no longer appropriate. Such a deconstruction seems to be a prerequisite for rebuilding a new model of a world that is more ft for purpose. p.18 (Peters et al., 2017)
In summary, both functional and structural plasticity of pyramidal neurons show a bell-shaped dependency on glucocorticoids. Thus, the high concentrations of glucocorticoids during stress and uncertainty allow the synaptic effcacy of apical tuft inputs to change over time. Moreover, stress and uncertainty lead to the shrinkage of the distal apical dendrites. In this way, both the generative model and the synaptic mechanisms of precision or gain control are disassembled. When stress is resolved and the situation is eased, the glucocorticoid concentrations fall: Then the (synaptic effcacy) parameters that learn the precision of prediction errors and the generative model are consolidated enabling the learning of an internal model when, and only when, they are capable of resolving uncertainty and stress. p.18 (Peters et al., 2017)

3.3. Habituation updating of goal states p.18 (Peters et al., 2017)

3.3.1. Habituation p.18 (Peters et al., 2017)

3.3.2. Habituators can tolerate stress p.18 (Peters et al., 2017)

In short, allostatic load can be averted at a subpersonal level by reducing the precision of one's prior preferences heuristically, adopting more realistic expectations about what can be achieved. p.19 (Peters et al., 2017)
Habituation as updating of goal states . Panel A. In non-habituators the goal states are f xed and remain unchanged. Panel B. In habituators the precision of goal states is relaxed. Even though, for a given strategy, the habituators beliefs about attainable states and the beliefs about goal states do not completely overlap, they still exhibit more overlap than non-habituators. Panel C. The KL divergences for each strategy are is smaller in habituators than in non-habituators. Thus, habituation decreases the risk. Panel D. Because a broadening of the beliefs about goal states reduces KL divergences, it changes the probability of each strategy that it may secure wellbeing. These changes in the probability distribution over strategies mean that habituators become more con f dent about what strategy to select. This explains why habituators exhibit smaller glucocorticoid responses than non-habituators. p.19 (Peters et al., 2017)

4. Allostatic load p.19 (Peters et al., 2017)

4.1. The plasticity and vulnerability of the brain p.19 (Peters et al., 2017)

#stress #plasticity #memory #learning With high glucocorticoid concentrations, postsynaptic dendritic spines are lost and dendritic branches shrink in various parts of the cortex and the hippocampus (Dias-Ferreira et al., 2009 Liston and Gan, 2011 Liston et al., 2006 Radley et al., 2006 Watanabe et al., 1992 Wellman, 2001). Fluctuating concentrations of glucocorticoids support a f ne-tuned interplay between spine formation, pruning and maintenance, whereas states of prolonged glucocorticoid exposure interrupt this interplay (Liston et al., 2013). p.19 (Peters et al., 2017)
#stress #sleep #plasticity Likewise, inappropriate updates can lead to disturbed sleep or bad dreams (Antonijevic, 2008 Rodenbeck and Hajak, 2001). While sleep normally serves to optimize and conserve our generative models (Hobson and Friston, 2012), poor sleep is likely to preclude the revision of inappropriate models (Wagner and Born, 2008). p.19 (Peters et al., 2017)
The healthy brain is resilient in the face of stressors and epigenetic cellular and molecular mechanisms produce continu- ous changes in gene expression. Thus, one cannot roll back the clock after stress is over, so that we must speak of resilience and recovery rather than reversal even though the alterations in neuronal structure and function may appear to have been reversed yet they are not the same as before (McEwen et al., 2015a,b McEwen and Morrison, 2013). Acute and chronic stress interferes with cognition, decision making, anxiety and mood, and in so doing affects systemic physiology through neuroendocrine, autonomic, immune and metabolic mediators and multi-morbidity of disorders frequently occurs (McEwen, 2007 McEwen et al., 2015b Rasgon and McEwen, 2016). p.20 (Peters et al., 2017)
Structural and functional allostatic plasticity is particularly evident in the #hippocampus, a key structure for episodic and spatial memory and mood regulation (McEwen, 2007 McEwen et al., 2015a). The hippocampus was the f rst brain structure outside of the hypothalamus found to possess stress and sex hormone receptors and it provided a gateway into the hormone sensitivity of the rest of the brain (McEwen et al., 2015b).# p.20 (Peters et al., 2017)
The #amygdala involved in fear, anxiety and aggression and the prefrontal cortex, important for working memory and executive function, both show functional and structural allostatic plasticity. In the amygdala, overlapping waves of excessively high concentrations of glucocorticoids and norepinephrine cause an extended window of excitability (Karst and Joels, 2016). Such a prolonged window of excitability is thought to contribute to the development of pathological conditions e.g., posttraumatic stress disorder (Karst and Joels, 2016). Basolateral amygdala neurons expand dendrites from chronic stress (Chattarji et al., 2015) while, as noted earlier, medial #PFC neurons, as well as hippocampal neurons, show dendritic shrinkage from the same stress (McEwen and Morrison, 2013). p.20 (Peters et al., 2017)

4.2. Non-habituators are fully exposed to toxic stress p.20 (Peters et al., 2017)

Because of continued uncertainty, the brain is constantly demanding for extra energy. Such an energy crisis with lack of habituation leads to allostatic load contributing to systemic and brain pathology. red{This energy crisis has two consequences: f rst, SNS/HPA-axis hyperactivity and second, metabolic alterations and stress-related health damaging behaviors (tobacco smoking, drinking alcohol, sleep deprivation). SNS/HPA-axis hyperactivity increases the risk of arterial blood ow turbulences, leading to atherosclerosis, thereby causing systemic and brain pathology (Peters and McEwen, 2015)./} p.20 (Peters et al., 2017)
Metabolic alterations and poor health behaviors lead to ineff cient mitochondrial metabolism, resulting in reactive oxygen species (ROS) and in ammation and worsen systemic and brain pathology (Picard et al., 2014). These pathologies include memory impairment, depression, myocardial infarction, stroke, visceral fat accumulation, type 2 diabetes, muscle loss, osteoporosis, disturbed growth and reproduction (McEwen, 1998). p.20 (Peters et al., 2017)

4.3. Comparison of mortality among habituators and non-habituators p.20 (Peters et al., 2017)

In summary, habituators can update their goal states as a consequence, they show attenuated responses when recurrently challenged, and in this way although they continue to live in the inhospitable environment they show a barely limited life expectancy. In contrast, non-habituators display a drastically shortened life expectancy but they are the individuals who would beneft most from stress-relief programs that include cognitive restructuring, social skills development, and mindfulness (Gulliksson et al., 2011 Orth-Gomer et al., 2009). p.21 (Peters et al., 2017)

5. Updating the stress defnition p.21 (Peters et al., 2017)

#stress #Fasting as referred to as the metabolic state achieved after complete digestion and absorption of a meal goes along with the activation of the SNS and HPA-axis. Such SNS- and HPA-axis activations serve to adequately supply the brain with energy (glucose, ketones, or lactate) (Kubera et al., 2012a, 2014). Since the brain-energy concentrations are tightly regulated (Oltmanns et al., 2008), such increases in SNS and HPA-axis activity are common in everyday life (Peters and Langemann, 2009). The Sel f sh Brain procures itself with energy (Peters et al., 2007b). After a few hours of fasting, we feel hunger and perceive SNS-induced interoceptive signals like nervousness, weakness, tremor, tachycardia, dizziness, and sweating. The (Sel f sh) Bayesian Brain uses perceptual inference to infer the cause (lack of thermodynamic energy) from the effect (the interoceptive signals). Then it uses active inference (food seeking behavior) to minimize variational free energy i.e., to eliminate the prediction errors (hunger, autonomic symptoms). If we are certain that food would be available soon, the appropriate action is selected and no stress occurs. However, if we are uncertain about whether we might get food at all, stress occurs as is the case in food insecurity (Bhattacharya et al., 2004). In such a case, uncertainty (entropy) monitored by the ACC stimulates the amygdala, and in so doing increases LC, SNS and HPA-axis activity in this way, stress facilitates the search for a novel strategy (Figs. 2 and 3). p.21 (Peters et al., 2017)
In light of the foregoing, we defne stress as the individual state of uncertainty about what needs to be done to safeguard physical, mental or social well-being. p.21 (Peters et al., 2017)

6. Conclusions p.21 (Peters et al., 2017)

Note: The is an optimal level of stress This analysis suggests that there is an optimum glucocorticoid level that enables the consolidation of activity-dependent plasticity (where the bell-shaped glucocor- ticoid-dependency curve has its peak) (Joels, 2006) that mediates experience-dependent learning when and only when, our generative models are suf f cient to resolve uncertainty, stress and elevated glucocorticoid levels. p.21 (Peters et al., 2017)
Finally, we considered long-term processes that could minimize variational free energy and stress by looking at the ultimate cause namely, the discrepancy between states that we can attain by acting on the world and the states we a priori expect to occupy (i.e., interoceptive, proprioceptive, emotional and prosocial goals). Our key observation is that exposure to chronic stress and the allostatic load that this entails can be remediated by revising our highest-level prior beliefs namely, prior expectations about the states we aspire to. This provides a nice metaphor that distinguishes between habituators and non-habituators in response to chronic stress. p.21 (Peters et al., 2017)
Note: Chronic stress leads to pathology Of note, individuals cannot always resolve uncertainty by reconstructing their internal model of the world. The inhospitable environment may also limit such a resolution, e.g. when the individuals live in poverty, in war- af icted areas, are long-term unemployed or victims of discrimi- nation (Kubera et al., 2016 Ludwig et al., 2012 Puhl and Heuer, 2009). If the resolution of uncertainty is achieved too late or is not possible at all, the adverse effects of the futile and brain-energy- consuming efforts for the resolution come to the fore, and the ongoing brain-energy crisis leads to allostatic load that contributes to systemic and brain pathology. Toxic stress refers to such a chronic condition in which the damaging effects of the stress responses prevail and the uncertainty can neither be resolved by a successful Bayesian update nor reduced by habituation. If, in such a situation, the brain gets stuck and does not recover, external intervention is required. p.22 (Peters et al., 2017)