511-fear-stress-reward

Fun

Fear

Animal models

http://www.cns.nyu.edu/labs/ledouxlab/images/image_research/fear_conditioning.jpg

Adapted from (Davis, 1992)

Amygdala circuits

(Medina, Repa, Mauk, & LeDoux, 2002)

• Direct (fast) pathways via thalamus
• Indirect (slower) pathways via cortex
• Input and output (behavior, physiology) specificity

Specificity of learning stimulus/response mappings

(Pellman & Kim, 2016)

• Specific stimulus/response, \(S \rightarrow R\), patterns
• Visual OR Auditory \(\rightarrow\) pain
• Taste \(\rightarrow\) nausea

Circuitry

(Brandão, Zanoveli, Ruiz-Martinez, Oliveira, & Landeira-Fernandez, 2008)

(Pellman & Kim, 2016)

• BLA, basolateral complex of the amygdala
• CEA, central nucleus of the amygdala
• ITC, intercalated cells of the amygdala
• PL, prelimbic cortex
• IL, infralimbic cortex
• HPC, hippocampus
• Thal, thalamus
• PAG, periaqueductal gray
• PBN, parabrachial nucleus

Stress types

• Acute stress
  • Short duration
• Brain detects threat
• Mobilizes physiological, behavioral responses
  • HPA (Cortisol), SAM (NE/Epi) axes
• vs. Chronic or stress
  • Long duration, persistent

(Kim, Pellman, & Kim, 2015)

Glucocorticoids

• Released by
  • Adrenal cortex
  • Other areas in small amounts
• Cortisol (hydrocortisone)
  • Increases blood glucose levels
  • Aids in fat, protein, carbohydrate metabolism
  • Suppresses immune system
  • Reduces inflammation
• Receptors in body and brain

(Kadmiel & Cidlowski, 2013)

• Multiple feedback loops
• Diurnal pattern

http://www.molecularbrain.com/content/figures/1756-6606-3-2-1-l.jpg

Impacts of chronic stress

• Major depressive disorder (MDD) & Post-traumatic Stress Disorder (PTSD)
  • Hippocampus and PFC volume reductions
  • Synapse loss
  • Reduced dendritic density

(Schmaal et al., 2016)

Cohen’s d-effect sizes 95% CI and for differences in subcortical brain volumes between major depressive disorder (MDD) patients and healthy control subjects. Effect sizes were corrected for age, sex and intracranial volume (ICV). The effect size for ICV was corrected for age and sex. P<0.05 corrected. CI, confidence interval.

(Schmaal et al., 2016)

(a) Cohen’s d-effect sizes 95% CI for differences in subcortical brain volumes between recurrent major depressive disorder (MDD) patients and healthy control subjects (striped pattern) and between first episode MDD patients and healthy controls (no pattern). (b) Cohen’s d-effect sizes 95% CI for differences in subcortical brain volumes between early onset (⩽21) MDD patients and healthy control subjects (no pattern) and between later onset (>21) MDD patients and healthy controls (striped pattern). Effect sizes were corrected for age, sex and intracranial volume (ICV). P<0.05 corrected, P<0.05. CI, confidence interval.

Impacts of acute stress

(Musazzi, Tornese, Sala, & Popoli, 2017)

“Figure 1. Neuroarchitectural Changes Induced by Repeated or Acute Stress in Rodents. (A) Repeated restraint stress (7 days) induces a reduction in the number and length of apical dendrites of pyramidal neurons (layer V) in the medial prefrontal cortex (PFC) of rats. (B) Magnified segment of dendrite from the same stressed rats, showing that repeated stress significantly decreases the number of spine synapses in medial PFC. (C) Reconstructions of representative infralimbic pyramidal neurons in mice exposed to zero (0), one (1×), or three (3×) unpredictable sessions of 10 min of forced swim stress. Apical dendritic branch length was significantly reduced after one or three stress episodes relative to controls. Adapted, with permission, from [24] (B) and [23] (C).”

(Musazzi et al., 2017)

Figure 3. Graphic Summary of Short- and Long-Term Functional and Neuroarchitectural Effects in Prefrontal Cortex (PFC) Synapses after Acute Footshock (FS) Stress [44]. The fast and transient increase in corticosterone (CORT) release induced by acute (40 min) FS stress was accompanied by the rapid increase in both depolarization-evoked and hypertonic sucrose-evoked (readily releasable pool) glutamate release in PFC, and the number of small excitatory synapses. The enhancement of glutamate release was sustained for up to 24 h, as well as the increased number of excitatory synapses, which normalized between 24 h and 7 days after FS. Before 24 h had elapsed from the start of FS stress, retraction of apical dendrites began and was sustained for up to 14 days. The timing of actual FS stress (40 min) is indicated by the red marker. Number of excitatory synapses and apical dendrite length are indicative and not in scale with other readouts. CORT and glutamate release data adapted from [44].

Changes in neural architecture

• Hippocampus (rich in CORT receptors)
• Prefrontal cortex

Neurochemical factors

• Cortisol enhances glutamate release
• Corticosteroid antagonists block this
• Ketamine (NMDA receptor antagonist) may act via similar mechanisms

Sapolsky, Why Zebras Don’t Get Ulcers

Pleasure/reward

From conceptual category to brain circuitry

(Kringelbach & Berridge, 2009)

Figure 1. Measuring reward and hedonia. Reward and pleasure are multifaceted psychological concepts. Major processes within reward (first column) consist of motivation or wanting (white), learning (blue), and – most relevant to happiness – pleasure, liking or affect (light blue). Each of these contains explicit (top rows, light yellow) and implicit (bottom rows, yellow) psychological components (second column) that constantly interact and require careful scientific experimentation to tease apart. Explicit processes are consciously experienced (e.g. explicit pleasure and happiness, desire, or expectation), whereas implicit psychological processes are potentially unconscious in the sense that they can operate at a level not always directly accessible to conscious experience (implicit incentive salience, habits and ‘liking’ reactions), and must be further translated by other mechanisms into subjective feelings. Measurements or behavioral procedures that are especially sensitive markers of each of the processes are listed (third column). Examples of some of the brain regions and neurotransmitters are listed (fourth column), as well as specific examples of measurements (fifth column), such as an example of how highest subjective life satisfaction does not lead to the highest salaries (top) [93]. Another example shows the incentive-sensitization model of addiction and how ‘wanting’ to take drugs may grow over time independently of ‘liking’ and ‘learning’ drug pleasure as an individual becomes an addict (bottom) [94].

Neuroanatomy of ‘pleasure’

(Kringelbach & Berridge, 2009)

Figure 2. Hedonic brain circuitry. The schematic figure shows the brain regions for causing and coding fundamental pleasure in rodents and humans. (a) Facial ‘liking’ and ‘disliking’ expressions elicited by sweet and bitter taste are similar in rodents and human infants. (b, d) Pleasure causation has been identified in rodents as arising from interlinked subcortical hedonic hotspots, such as in nucleus accumbens and ventral pallidum, where neural activation may increase ‘liking’ expressions to sweetness. Similar pleasure coding and incentive salience networks have also been identified in humans. (c) The so-called ‘pleasure’ electrodes in rodents and humans are unlikely to have elicited true pleasure but perhaps only incentive salience or ‘wanting’. (d) The cortical localization of pleasure coding might reach an apex in various regions of the orbitofrontal cortex, which differentiate subjective pleasantness from valence processing for aspects of the same stimulus, such as a pleasant food.

• Analogous circuits mediating facial expressions of “liking” and “disliking”

Reward

• A reward reinforces (makes more prevalent/probable) some behavior
• Milner and Olds (Milner, 1989) discovered ‘rewarding’ power of electrical self-stimulation
• (Heath, 1963) studied effects in human patients.

Electrical self-stimulation

“Reward” circuitry in the brain

(Nestler & Carlezon, 2006)

• Lateral Hypothalamus (Hyp)
• Medial forebrain bundle (MFB)
• Ventral tegmental area (VTA) in midbrain
• Nucleus accumbens (nAcc)
• Dorsal Raphe Nucleus/Locus Coeruleus (DR/LC)
• Amygdala (Amy)
• Hippocampus (HP)
• Prefrontal cortex (PFC)

(Kohls, Chevallier, Troiani, & Schultz, 2012)

What does DA signal?

• Hedonia and anhedonia
• Incentive salience
• Reward prediction error (RPE)

(Hu, 2016)

DA and GABA signaling

(Watabe-Uchida, Eshel, & Uchida, 2017)

Figure 1. Firing patterns of identified dopamine and GABA neurons in VTA. (a) VTA neurons were recorded while mice performed an odor-outcome association task in which different odors predicted different outcomes (see legend on right). Odors were presented for 1 s (gray shading), and outcomes were presented after a 1-s delay. Neuron types were identified based on their optogenetic responses. Dopamine neurons (left) showed phasic excitations to reward-predictive cues and reward. GABA neurons (right) showed sustained activation during the delay. Data from Cohen et al. (2012). (b) Reward expectation modulates dopamine neuron firing. The plot on the left shows when outcome was presented, and the right-hand plot shows when outcome was omitted. Different odors predicted reward with different probabilities. Higher reward probability increased cue responses but suppressed reward responses. Data from Tian & Uchida (2015). Also see Fiorillo et al. (2003) and Matsumoto & Hikosaka (2009a,b). (c) Reward context-dependent modulation of dopamine responses to air puff–predictive cues and air puff. The task conditions during recording differed only in the probability of reward. Dopamine neurons showed both excitation and inhibition in high-reward contexts (left) but only inhibition in low-reward contexts (right). The response in reward trials is not shown. Data from Matsumoto et al. (2016). Abbreviations: CS, conditioned stimulus; VTA, ventral tegmental area.

Expectation modulates DA signaling

(Watabe-Uchida et al., 2017)

Figure 2. Subtractive computation in dopamine neurons. (a) In one task condition (no odor, black), different amounts of reward were presented without any predictive cue. In another condition (odor A, orange), the timing of reward was predicted by an odor. (b) Prediction. Division should change the slope of the curve, whereas subtraction should cause a downward shift. (c) Average response of 40 optogenetically identified dopamine neurons. Prediction caused a subtractive shift. Data from Eshel et al. (2015). (d) Three example neurons. Although individual neurons exhibited diversity with respect to response magnitudes, their response functions were scaled versions of one another. Data from Eshel et al. (2016).

DA network

(Watabe-Uchida et al., 2017)

(Watabe-Uchida et al., 2017)

Reward & Aversion Networks

(Watabe-Uchida et al., 2017)

Psychopharmacology of pleasure

• Dopamine
• Serotonin, Norepinephrine
• ACh

(Cock, Vidailhet, & Arnulf, 2008)

• Opioids, endogenous morphine-like NTs (endorphins)

(Clapp, Bhave, & Hoffman, n.d.)

• Cannabinoids = psychoactive compounds found in cannibis
• Endocannabinoids (endogenous cannabinoid system)
  • Cannabinoid CB1 receptors in CNS; CB2 in body, immune system

(Flores, Maldonado, & Berrendero, 2013)

References

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Clapp, P., Bhave, S. V., & Hoffman, P. L. (n.d.). How Adaptation of the Brain to Alcohol Leads to Dependence. Retrieved from http://pubs.niaaa.nih.gov/publications/arh314/310-339.htm

Cock, V. C. D., Vidailhet, M., & Arnulf, I. (2008). Sleep disturbances in patients with parkinsonism. Nature Clinical Practice Neurology, 4(5), 254–266. https://doi.org/10.1038/ncpneuro0775

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Heath, R. G. (1963). Electrical self-stimulation of the brain in man. American Journal of Psychiatry, 120(6), 571–577. https://doi.org/10.1176/ajp.120.6.571

Hu, H. (2016). Reward and aversion. Annual Review of Neuroscience, 39, 297–324. https://doi.org/10.1146/annurev-neuro-070815-014106

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Kohls, G., Chevallier, C., Troiani, V., & Schultz, R. T. (2012). Social ‘wanting’dysfunction in autism: Neurobiological underpinnings and treatment implications. Journal of Neurodevelopmental Disorders, 4(10), 1–20. https://doi.org/10.1186/1866-1955-4-10

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Milner, P. M. (1989). The discovery of self-stimulation and other stories. Neuroscience & Biobehavioral Reviews, 13(2–3), 61–67. https://doi.org/10.1016/S0149-7634(89)80013-2

Musazzi, L., Tornese, P., Sala, N., & Popoli, M. (2017). Acute or chronic? A stressful question. Trends in Neurosciences. https://doi.org/10.1016/j.tins.2017.07.002

Nestler, E. J., & Carlezon, W. A. (2006). The mesolimbic dopamine reward circuit in depression. Biological Psychiatry, 59(12), 1151–1159. https://doi.org/10.1016/j.biopsych.2005.09.018

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Schmaal, L., Veltman, D. J., Erp, T. G. M. van, Sämann, P. G., Frodl, T., Jahanshad, N., … Hibar, D. P. (2016). Subcortical brain alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group. Molecular Psychiatry, 21(6), 806–812. https://doi.org/10.1038/mp.2015.69

Watabe-Uchida, M., Eshel, N., & Uchida, N. (2017). Neural circuitry of reward prediction error. Annual Review of Neuroscience, 40, 373–394. https://doi.org/10.1146/annurev-neuro-072116-031109