Friday, 6 July 2018

Forgotten Memories Brought Back in Mice? Hold Up...

A study published yesterday in Cell[1] has purportedly found that memories formed as infants may be able to be retrieved as adults using optogenetic techniques, and various media outlets have enthusiastically inferred that this suggests we may be able to remember the events from our infancy, some even suggesting we may be able to remember our own birth. 

The study, led by psychologist Paul Frankland, was based on previous research by the group which found that the “forgetting” of memories during infancy may be a result of high levels of hippocampal neurogenesis at this age – i.e. the neurons representing the memories are replaced by new neurons, thus erasing the memories. 

The aim of the current study was to determine if the memories formed during infancy are permanently lost due to a failure in encoding during infancy, or become progressively inaccessible over time due to a progressive loss in the ability to retrieve them as the mice age. 

The researchers placed young mice into a box and gave them a foot-shock, so that when they are placed back into the box they freeze in anticipation of the shock - a classic fear-based training paradigm. The mice had been engineered to contain a specific set of light-sensitive neurons in a particular region of the hippocampus involved in the formation of memories - the dentate gyrus - which allowed the researchers to activate these neurons by firing a laser at them. They then placed the same mice back into the box as adults and activated this set of neurons, thus reinstating the memory and causing the mice to freeze in anticipation of the shock. 

The authors seem to suggest that this means that some hidden traces of the memories created during infancy were retained and were able to be recalled by the researchers by optogenetically activating the specific set of hippocampal neurons which were observed to be activated during the contextual fear encoding (the “dentate gyrus encoding ensemble”), positing that the reactivation of these ensembles was sufficient for “memory recovery” in adulthood. 

They then quantified the activity of an activity-regulated gene, c-Fos, in cortical and subcortical brain regions following the fear learning event. The purpose of this was to determine whether fluorescently-tagged neurons activated during the memory encoding event were preferentially reactivated during the “memory recall” in adulthood – which of course they were, implying successful recall of the memory. 

However, while they may have been able to reactivate a neural pathway they have essentially programmed into the mice’s brains during infancy, I’m not so sure it was the actual memories themselves which were “recovered”. 

It’s worth nothing that these are memories which the researchers have created in the mice by giving them foot-shocks within a particular environmental context; they are not naturally formed memories. 

“When the infant mice were placed in the box and the laser was turned on, the animals’ memories of the electric shock returned and they froze in place.” 

The fact that the mice freeze when they are placed into the same environment in which they were given a foot-shock during infancy does not necessarily mean that they remember receiving the foot-shock. It simply means that a particular behaviour (freezing) has been programmed into their brains and artificially re-instated by firing lasers at the neurons underlying this behaviour. It means that the same neural pathways resulting in a fear-based freezing response were activated – but these may be totally separate from the pathways containing the actual memory of the event (if they even exist). Besides, we are talking about a simple, conditioned response here; an instinctual behaviour in response to pain – much like you learn to quickly move your hand away from a hot plate – not an actual subjective, detailed memory of an event. It’s possible that the mice would freeze if placed in an entirely different context and the same light-sensitive neurons artificially reactivated.

“To first induce memory formation in the animals, the scientists placed the mice in a box and gave them a mild foot shock. While young adult mice retained this memory and froze when put in the box a second time, infant mice forgot this fear-related memory after a day and behaved normally when they encountered the box again” 
Percent freezing levels declined with retention delay in P17, but
not P60, mice. From Guskjolen et al., 2018, Figure 1(B).

So the infant mice were not forming the memories? This appears to contradict the conclusions of the study – i.e. that those memories are simply hard-to-retrieve; hidden deep within the brain, unable to be recovered by natural cues, and that direct stimulation of the engram (in combination with re-exposure to the training context) may reinstate the connections, leading to memory recovery. How can we say that the memories are simply difficult to retrieve when we are unsure if they were ever formed in the first place? 

“However, we found that opto-stimulation of neural ensembles that were engaged during training was sufficient to induce conditioned freezing at the same retention delays. These results suggest that the underlying engram corresponding to the fear conditioning event is not completely overwritten. Rather, this engram presumably exists in an otherwise inaccessible, dormant state, in which “natural” reminders (such as exposure to the training context) most often do not induce successful reactivation(...) This pattern of results is reminiscent of other amnestic states, including mouse models of retrograde amnesia and Alzheimer’s disease, in which opto-stimulation of tagged encoding ensembles (but not presentation of natural cues alone) permits memory recovery." 

Again, the conclusions drawn assume that the artificially activated ensembles encode the actual memory of the event itself – which is not only not confirmed, but hard to believe considering when the mice were put back in the box the second-time as infants, they had not remembered the fear-related memory supposedly created the day before. How, then, do we know that the memory was encoded at all? How do we know it is the memory that is recalled, and not simply a programmed, artificially instated fear-response? This is too simplistic of a model to draw such far-fetched conclusions, and we certainly can’t say that this type of “forgetting” in infancy is akin to other types of amnesia such as that in Alzheimer’s disease. They are completely different processes, at completely different ages. 

Furthermore, less cortical “re-engagement” was observed following optogenetic stimulation of the dentate gyrus engrams in mice trained as infants compared to those trained as adults, further highlighting the possibility that the memories which were purportedly retrieved may not have been formed at all in the infants. 

“Indeed, whereas adult contextual fear memories are successfully consolidated over the course of weeks, equivalent infant memories are being actively forgotten during this period and therefore perhaps not successfully consolidated in the cortex (…) opto-stimulation of tagged dentate gyrus ensembles leads to recovery of an engram that is qualitatively different (and likely impoverished) compared to the equivalent representation in adult animals.” 

The authors even concede that the “memory recovery” did not persist into the light OFF periods – i.e. when the trained mice were placed into the box as adults, they did not freeze unless the hippocampal engrams engineered to be light-sensitive were activated by the researchers – a pattern which has been observed in similar studies involving reactivation of tagged engram cells in the dentate gyrus [2–7]

While the study further adds weight to the idea that infantile forgetting is likely due to a failure of memory encoding in the infant brain (something which we knew anyway), the methods used are simply insufficient to be able to draw some of the conclusions the authors propose, and the study certainly does not suggest that we may be able to recover our infantile memories anytime soon.


 References: 

[1] A. Guskjolen, J.W. Kenney, J. de la Parra, B.A. Yeung, S.A. Josselyn, P.W. Frankland, Recovery of “Lost” Infant Memories in Mice, Current Biology. 0 (2018). doi:10.1016/j.cub.2018.05.059.
[2] X. Liu, S. Ramirez, P.T. Pang, C.B. Puryear, A. Govindarajan, K. Deisseroth, S. Tonegawa, Optogenetic stimulation of a hippocampal engram activates fear memory recall, Nature. 484 (2012) 381–385. doi:10.1038/nature11028.
[3] T. Kitamura, S.K. Ogawa, D.S. Roy, T. Okuyama, M.D. Morrissey, L.M. Smith, R.L. Redondo, S. Tonegawa, Engrams and circuits crucial for systems consolidation of a memory, Science. 356 (2017) 73–78. doi:10.1126/science.aam6808.
[4] D.S. Roy, S. Muralidhar, L.M. Smith, S. Tonegawa, Silent memory engrams as the basis for retrograde amnesia, Proc. Natl. Acad. Sci. U.S.A. 114 (2017) E9972–E9979. doi:10.1073/pnas.1714248114.
[5] T.J. Ryan, D.S. Roy, M. Pignatelli, A. Arons, S. Tonegawa, Memory. Engram cells retain memory under retrograde amnesia, Science. 348 (2015) 1007–1013. doi:10.1126/science.aaa5542.
[6] D.S. Roy, A. Arons, T.I. Mitchell, M. Pignatelli, T.J. Ryan, S. Tonegawa, Memory retrieval by activating engram cells in mouse models of early Alzheimer’s disease, Nature. 531 (2016) 508–512. doi:10.1038/nature17172.
[7] S. Ramirez, X. Liu, P.-A. Lin, J. Suh, M. Pignatelli, R.L. Redondo, T.J. Ryan, S. Tonegawa, Creating a false memory in the hippocampus, Science. 341 (2013) 387–391. doi:10.1126/science.1239073.

Read more »

The Future of Neuroscience


In the current animal model of depression, a mouse is placed in a jar of water and struggles to swim to avoid drowning, in the aptly named “forced-swim” test(1). After a few minutes, the mouse stops trying to escape, instead choosing to float immobile in the water. At this point, the mouse is said to experience “behavioural despair” (the mouse loses hope to escape the stressful environment) and the mouse is then classified as suffering from “depression”. This is the standard model used to test antidepressant drugs – the time spent immobile versus swimming in mice given the drug is compared to that of controls. Clearly, this is a simplistic model with very little resemblance to the highly complex, multi-faceted disorder of clinical depression in humans. Yet, although its efficacy has long been contested(2), this is still the most common mouse model used in the majority of research into “depression”. 

This is not just the case for depression. Many psychiatric disorders are still studied using simplistic animal models which – though important – essentially bear little resemblance to the experiences of those suffering from the disease on a daily basis. 

On top of this there, is a pervasive disconnect between psychology and neuroscience. It is fundamentally impossible to study affective or cognitive phenomena such as emotion or foresight using animal models, since mice, rats and monkeys are all unable to communicate what they are feeling with humans. Instead, researchers study behaviours as a proxy – for instance, what a mouse does before it gets a reward, which is largely a matter of interpretation. But we lack a way to study actual emotions in mice – which, besides, are likely vastly different subjective experiences to those of humans. While mouse models have their uses, they are generally an insufficient representation of brain disease or even normal brain function in humans. 

It is without surprise then, that over the past 40 years there has been little improvements in the outcomes of patients with the most common brain diseases. Some pharmaceutical companies are abandoning research into drugs for psychiatric diseases altogether due to the high cost and low success rate. For example for Alzheimer’s, every time we think we have a promising new drug in development to break down the toxic amyloid plaques, we find that it fails in clinical trials, and moreover, we find that we were coming at the problem from the wrong angle altogether(3). We now know that we need to intervene long before amyloid deposits are prevalent, and long before symptoms are seen. Some research shows a portion of patients diagnosed with Alzheimer’s do not even have significantly more amyloid-β plaques in their brains than healthy controls, and amyloid pathology has been observed in cognitively healthy elderly individuals, suggesting that amyloid-driven tauopathy may at best only be part of the problem. Thus, we currently lack even an effective diagnostic criteria for neurodegenerative disorders. Treatment for Alzheimer’s is largely symptomatic; we are a long way off from understanding the root causes of the disease. Progress is slow, but we are learning. 

There is also the problem of brain scanning. The most prevalent form of brain imaging in neuroscience and psychology is undoubtedly functional magnetic resonance imaging (fMRI). However, again, this is a proxy – fMRI measures blood flow across the brain while the subject is engaged in a particular task or activity; it does not directly measure neuronal activity(4). One unpublished study from 2009 found apparent cognitive activity in the brain of a dead salmon(5), highlighting the risk of false positives in fMRI studies. Similarly, electroencephalography (EEG) measures electrical activity at the brain surface – however it lacks specificity in that it does not measure the activity of specific neurons or sets of neurons, but rather of a crude combination of electrical currents across a particular brain area. Unfortunately, it is not yet possible to measure the activity of a specific set of neurons in living, human brain tissue. 

However, a small minority of forward-thinking neuroengineers are currently working on measuring real-time electrical brain activity in vivo, in humans. This is already possible in the brains of mice and in monkeys, but not yet in humans. Thus, hopefully in the not-too-distant future, we will be able to record activity from specific sub-sets of neurons and correlate this with not only behaviour, but with thought, emotion and, of course, depression. 

With a little imagination, let’s fast forward 100, or perhaps only 50 years. We now understand the root causes of Alzheimer’s, Parkinson’s, depression, schizophrenia etc., and are able to deliver targeted genetic or drug therapies, custom-made for each patient, to treat brain disease both symptomatically, and more importantly, prophylactically. Furthermore, we now understand that these disorders which we considered one disease, were in fact different diseases with similar symptoms but vastly different biological causes, each requiring a different treatment. We will look back to the primitive days of neuroscience – the early 21st century – and be amazed that the majority of brain diseases were being treated with the wrong drugs, which were more often than not completely ineffective, or even counter-productive(6,7).

In order to achieve this, we first had to figure out how to get electrodes through the skull and into the brains of healthy, living humans, without causing any risk to the subject. Rather than drilling holes through the skull, we use microelectrodes so small that they can be inserted without rupturing any blood vessels, thus avoiding the risk of stroke. We might even use lasers. As technology progresses, we will be able to record from thousands of electrodes at once using smart, robotic, microscopic implantations which work their way around blood vessels and through the brain tissue. Eventually, this will be possible using wearable devices which the subject can implant into their brain and go about their day, while the device is constantly collecting and uploading high-resolution neuronal activity directly from their brain to the computer of a researcher, or their doctor, for analysis. Combined with powerful yet harmless lasers able to pass through the skull and produce images of neurons and synapses with sub-cellular precision, we are able to decipher not only the connectome, but the precise patterns of activity between specific neurons during a particular function, on an individual level, for each patient. By collecting masses of data from millions of patients, we can mathematically calculate – with the aid of superfast computers – what exactly is going wrong in neurological diseases such as Alzheimer’s. Furthermore, we have finally managed to bridge the gap between psychology and neuroscience, by being able to ask the patients about their emotional, subjective experiences, and correlate this with their neuronal activity at that exact point in time. 

At some point, these wearable devices will become commercialised by the likes of Google, Amazon and Apple, offering free services to customers in exchange for their private data – their thoughts. Having learned from our mistakes in the early 21st century regarding privacy and data harvesting, customers will demand rights and legislation to decide how their personal brain activity is used by multinational corporations. However, this will prove ineffective, and customers will willingly sacrifice their privacy anyway, by updating to the latest version of Apple iBrain® without reading the terms and conditions. This will open up a whole Pandora’s box of neuro-hacking and neuro-spyware, as well as further driving inequality and elitism – since only those in first-world countries can access the devices, and only the wealthiest of those can afford the latest and greatest bio-upgrades. The societal, political and economic ramifications of this could fill an entire book in and of itself. But from a neuroscientific perspective, this will be a turning-point; a revolution in neuroscience research, allowing for not only the enhancement of normal brain function – or biohacking – but also significant advances in the treatment of brain diseases. Alzheimer’s, schizophrenia, autism, ADHD, addiction, depression and anxiety disorders will all be things of the past. 

So too will smartphones. Generation Y will tell their kids, “I remember when we had to type our text messages with our thumbs, or ask Alexa to add vegan meat to the shopping list. We never had Google Think® in my day”, or “I remember when we had to go to college to study for years, we had to sit down and read books to learn things. We never had Amazon HiveMind® in my day”. Meanwhile their kids seamlessly communicate via Apple iThought®, video chat via Skype Hologram®, and instantaneously download entire textbooks and literature via an ultra-fast 100Gb/s subscription to Amazon’s entire library for only $19.99 per month. Fake news will become a thing of the past, as every news article you download is instantly verified against thousands of peer-reviewed sources – reviewed both by humans and by sophisticated AI technology. You will never forget anything ever again, as any memory you choose to remember will be uploaded to the cloud, ready to be accessed and relived at will. Alternatively, should you choose, you can delete a traumatic or stressful memory, like it never happened. Without delving too far into the realm of science fiction, the possibilities are Limitless®. Anything is possible, so long as we can dream it – or Google DeepDream® it. 

Our knowledge of neuroscience is only in its infancy. Our understanding the human brain in all its complexity is only a mere few steps away from exponential growth. As technology combines with neuroscience, we become ever closer to understanding ourselves, and to an entirely interconnected consciousness. Societies working together as a collective intelligence are capable of amazing things – just look at bees and ants. Times are changing, for better or for worse.

Now, back to those mice...


References: 

1. Can, Adem, Dao, David T., Arad, Michal, Terrillion, Chantelle E., et al. (2012) ‘The Mouse Forced Swim Test’. Journal of Visualized Experiments : JoVE, (59). [online] Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3353513/
2. Borsini, Franco, Volterra, Giovanna and Meli, Alberto (1986) ‘Does the behavioral “despair” test measure “despair”?’ Physiology & Behavior, 38(3), pp. 385–386.
3. Castello, Michael A., Jeppson, John David and Soriano, Salvador (2014) ‘Moving beyond anti-amyloid therapy for the prevention and treatment of Alzheimer’s disease’. BMC Neurology, 14, p. 169.
4. Ekstrom, Arne (2010) ‘How and when the fMRI BOLD signal relates to underlying neural activity: The danger in dissociation’. Brain Research Reviews, 62(2), pp. 233–244.
5. Scicurious (2012) ‘IgNobel Prize in Neuroscience: The dead salmon study’. Scientific American Blog Network. [online] Available from: http://blogs.scientificamerican.com/scicurious-brain/ignobel-prize-in-neuroscience-the-dead-salmon-study/ (Accessed 27 April 2016)
6. Anon (2016) ‘Most antidepressant drugs ineffective for children and teens, study finds’. University of Oxford. [online] Available from: http://www.ox.ac.uk/news/2016-06-08-most-antidepressant-drugs-ineffective-children-and-teens-study-finds (Accessed 6 July 2018)
7. Cipriani, Andrea, Zhou, Xinyu, Giovane, Cinzia Del, Hetrick, Sarah E., et al. (2016) ‘Comparative efficacy and tolerability of antidepressants for major depressive disorder in children and adolescents: a network meta-analysis’. The Lancet, 388(10047), pp. 881–890. 


Read more »

Friday, 3 March 2017

The Role of Stress in Drug Addiction

In 1935, Alcoholics Anonymous was founded, with the goal of understanding and treating the “intrapsychic” and social forces causing addiction (1). Almost a century later, research on drug addiction has progressed a long way and remains a key area of psychology/neuroscience research. In the 1970s, researchers began to study the neurochemical processes underlying addiction. Prior to this it was assumed that addiction was the result of “chemical hooks” which exist in addictive substances (2), but this period marked a paradigm shift and a new wave of research in which the body’s own biochemical system was considered responsible for producing addictive behaviours, supported by the observation that people can become addicted to a wide range of non-drug related behaviours including sex, gambling and frivolous spending (3). 

A plethora of experiments throughout the 1950s and 60s on rats (4), pigeons (5) and monkeys (6) seemingly proved, by administering various drugs of abuse to the animals often using an operant conditioning model, that the animals would self-administer the drugs compulsively, often resulting in addiction, overdose and/or death. However, the studies, especially on rats, all shared a common flaw – the animals were kept in solitary confinement in small cages with the option of either drugs or saline. They were placed in stressful conditions and given a choice between a pleasurable / reinforcing reward or no reward. Unsurprisingly, in almost all cases they continually self-administered the drugs. These experiments therefore better represent a model of high-risk human groups, rather than the general population (7). A researcher named Prof. Bruce Alexander noticed this and carried out the experiments differently. He repeated the experiments using the same methodology, except that post-weaning, some of the rats were moved to luxurious, spacious cages filled with obstacle courses and, crucially, other rats to form social bonds with; an ideal, stress-free environment. A further group of rats were weaned entirely in the “colony housing” environment. In these experiments, all the rats in the colony housing did not compulsively self-administer morphine, whereas the rats kept in isolation self-administered up to sixteen times as much (8,9); subsequent research also indicated a role of social isolation in morphine self-administration (10). Furthermore, there are multiple studies showing that (isolated) rats preferentially choose sweetened water over cocaine (11,12), and rats with sectioned glossopharyngeal nerves reject morphine solution (13). Alexander thus proposed a shift from exposure orientation – that an individual who uses addictive drugs more than once will inevitably become addicted – to adaptive orientation – the notion that addiction is likely only under certain circumstances, such as when concomitantly faced with severe distress and rewarding drugs (14). Indeed, despite many Vietnam war veterans becoming addicted to heroin while at war – an extremely stressful environment – very few relapsed into addiction once returning home to their families (14), and many recreational opiate users never fall into addiction (15)

Haney et al. (16) examined the effect of a social stressor – repeated exposure to aggressive attack by a same-sex opponent – and found that rats which had experienced the social stressor self-administered more cocaine than controls, with this effect becoming more pronounced over time. Another study found that rats exposed to a mild stressor (a foot-shock) prior to each drug session self-administered significantly more heroin than controls, implicating that exposure to stress appears to enhance the reinforcing effects of heroin (17). This finding is strongly corroborated in humans. 

Stressful life events play an “integral role” in theories of drug addiction (18). Interestingly, studies as far back as the 1980’s have shown that minor, daily stressors have a greater negative impact on an individual’s overall well-being than major stressful life events (19,20), and that negative affective states such as anxiety are closely correlated with higher rates of cravings and relapse in alcoholism (21). In fact, the number of minor stressors and the perceived severity of these seems to predict the rate of cravings experienced by an individual undergoing addiction treatment (18).  Recent findings show that opium addicts experience a higher rate of psychosocial stressors, and make significantly less use of problem-focused coping methods (restraint coping, seeking social support etc) and significantly more use of less useful coping methods including venting and mental disengagement, in comparison to normal subjects (22). It has previously been shown that individuals who experience higher levels of negative effect are more likely to use drugs or alcohol as coping mechanisms to provide distraction from unpleasant emotions (23). Furthermore, the negative reinforcement model of addiction – one of the most widely studied – posits that once an individual experiences physical dependence, their drug use is primarily motivated by the desire to avoid the negative affective states associated with withdrawal (24). However, alternative theories propose that individuals who report experiencing more stressful events are more likely to experience cravings because they have a heightened attention to stressors, including cravings (18). Regardless, research has proposed that some of the most effective ways to reduce the impact of stress on cravings and relapse is by providing effective social support, combating high levels of stress – through healthy, problem-focused coping methods – as well as addressing stressful environmental factors such as unemployment (18,25). This is again an example of a shift from the view that addictive drugs cause addiction, to the view that the individuals own biochemistry – which in turn is highly dictated by personal circumstance/stress – is responsible for the formation of addictive behaviours, and that effective treatments for drug addiction should therefore focus on correcting both the individuals stressful circumstances and teaching healthier, more effective coping mechanisms to manage stress throughout (and beyond) the addicts’ recovery. 

So why is it that some individuals appear to be naturally more vulnerable to addiction than others? As well as obvious environmental factors – such as stressful environments and availability of drugs as opposed to other, non-drug rewards (7) – there appear to be numerous biochemical differences between individuals who show a predisposition towards ineffective, harmful mechanisms of dealing with stress (e.g. drugs), and those who are less susceptible to drug addiction and more likely to utilise healthy coping mechanisms. Indeed, in both humans and animals, the propensity to develop compulsive self-administration of rewarding drugs under stable laboratory conditions varies among individuals with equal access to the drugs (26). It has even been shown that pre-natal stress can play a role in the likelihood of developing drug addiction. Individuals predisposed to addiction show a higher behavioural locomotor reactivity to both novelty and psychostimulant drugs (27,28) (in contrast, individuals which show a low behavioural and hormonal reactivity to novelty will self-administer amphetamine initially, but will not maintain this behaviour and become addicted (29)), and prenatal stress is correlated with both an increased locomotor reactivity to drugs and novelty compared to controls, and an increased level of amphetamine self-administration in rats (30). However, the predisposed animals also show a greater release of corticosterone under these conditions (27), an apparently innate biochemical variance between predisposed individuals and those not predisposed to addiction. Corticosterone is the primary corticosteroid hormone produced in the adrenal cortex of rats, analogous to cortisol in humans (although corticosterone is present in human cerebrospinal fluid, it only has significant effects on neuroendocrine function in supraphysiological concentrations (31)). 

Increased levels and duration of corticosterone secretion, either intrinsically or induced by stress has been shown to not only increase the predisposition of rats to develop drug addiction (26), but to predict individual vulnerability (29). Furthermore, corticosterone administration in non-predisposed rats (low responders, LR) was directly correlated with increased amphetamine self-administration in those individuals, and even re-established self-administration in LR rats which had previously ceased self-administration (29), strongly suggesting a role of the hypothalamic-pituitary-adrenal (HPA) axis in the pathogenesis of addiction. Additionally, Lewis rats appear to show a greater vulnerability to compulsive self-administration of reinforcing drugs (32,33), and also exhibit lower basal corticosterone levels (34)

It is hypothesised that this association between higher levels of corticosterone and a greater vulnerability to drug addiction is due to an interaction between corticosterone and the dopaminergic (DA) system. There are three main pillars for this argument: (i) many rewarding drugs of abuse, especially psychostimulants, act on the DA system to exert their reinforcing effects (35,36) – this is the foundation of the extensively studied dopaminergic hypothesis of addiction which garnered a great deal of support over the last few decades (37) – and rats which show a high vulnerability to addiction exhibit a 250% higher basal dopamine concentration in the nucleus accumbens (NAc) than LR rats (38); (ii) DA neurons express corticosterone receptors (39) and (iii) corticosterone – both secreted endogenously in response to stress or administered exogenously, mimicking the intrinsic stress response – appears to directly stimulate DA neurons (40), and furthermore is necessary for the sensitisation of the DA system induced by amphetamine to occur (41), and subsequently modify the functioning of the HPA axis (42). Further evidence of the involvement of the HPA axis in addiction is afforded by a series of studies in which adrenalectomy – the removal or inhibition of the adrenal glands – significantly reduced the response to cocaine in rats (26) and induced a reduction in self-administration of the drug (43). A bilateral adrenalectomy completely abolished self-administration and, significantly, this effect was reversed when corticosterone was administered to the rats via their drinking water (43). This effect was also observed in ethanol addicted rats (44), as well as morphine dependent rats (45), confirming that this is not a phenomenon unique to cocaine. Furthermore, chronic ethanol exposure leads to significant alterations in corticosterone levels and dysregulation of the HPA axis (46). These findings are significant because it is known that chronic exposure to stress can also lead to alterations in the homeostatic functioning of glucocorticoids, thus leading to alterations in DA neuronal function (47), which itself is strongly implicated in the pathogenesis of addiction (37). 

Figure 1: Major components of the stress response mediated
 by the hypothalamic–pituitary–adrenal (HPA) axis.
From Stephens & Wand, 2012. 
The HPA axis refers to a neuroendocrine system composed of three components which together mediate the primary physiological response to stress – the paraventricular nucleus (PVN) of the hypothalamus, the anterior lobe of the pituitary gland, and the adrenal gland, together forming the hypothalamic-pituitary-adrenal (HPA) axis. In response to stress, the hypothalamus releases corticotrophin-releasing factor (CRF), which on binding to receptors in the pituitary, induce the release of adrenocorticotropic hormone (ACTH), which in turn binds to receptors in the adrenal cortex ultimately stimulating the release of glucocorticoids, primarily cortisol in humans or corticosterone in rats (48). It is known that cortisol, like corticosterone, can influence a individuals cognitive processes, promoting reward learning, which may play a role in relapse in addiction (48). Cortisol has been reported as an indicator of DA sensitivity and a predictor of the amount of craving for nicotine (49) and is implicated in the rewarding effects of other reinforcing substances including psychostimulants (50). Furthermore, stress-induced CRF is also considered a predictor of relapse, and is thought to be responsible for some of the stressful effects of withdrawal, including an increase in anxiety (51). CRF is known to play a crucial role in smoking addiction – along with Neuropeptide Y, the orexins, and norepinephrine – by briefly decreasing subjective stress levels but over time leading to a dysregulation of brain systems underlying stress (52), similar to findings with chronic alcohol exposure (46)

The hypothalamus component of the HPA axis is modulated by both diurnal and metabolic signals, as well as by the limbic system and prefrontal cortex. Chronic alcohol and nicotine use is known to induce modifications in these frontal–limbic interactions which may account for the HPA response differences observed in chronic users (53), and nicotine withdrawal is associated with a dramatic decrease in cortisol levels (54). Thus, it appears that glucocorticoids including cortisol / corticosterone – the primary mediators of the stress response – are functionally linked with the DA system, and that their functioning is altered by chronic use of addictive substances, which may be important in the pathophysiology of drug addiction. 


Figure 2: Dysregulation of brain systems underlying
stress by smoking addiction. From Bruijnzeel, 2012.

This has been proposed in the literature over a decade ago by Piazza & Le Moal (26). In addition to observations that animals showing a high vulnerability to addiction exhibit a 250% higher basal dopamine concentration in the NAc than non-predisposed animals (38), suppression of corticosterone by adrenalectomy almost halved the NAc extracellular dopamine concentration, as measured by microdialysis (55). Furthermore, restoring the basal levels of the hormone completely reversed this effect. Various mechanisms have been proposed regarding the biochemical pathways by which glucocorticoids modulate (up-regulate) the DA system. Firstly, corticosterone appears to up-regulate the synthesis of dopamine via interactions with tyrosine hydroxylase, an enzyme which converts the amino acid L-Tyrosine to L-DOPA, the precursor to dopamine (56,57). Second, glucocorticoids have been shown to decrease monoamine-oxidase activity (58,59), the key enzyme which catalyses the oxidative break-down of dopamine. Thirdly, glucocorticoids may decrease dopamine reuptake at the synaptic cleft (60), thus enhancing the effects of dopamine. Drugs of abuse with different mechanisms of action, as well as stress, all act to increase the strength of excitatory synapses in midbrain DA neurons; however drugs with little or no abuse potential do not. Moreover, the effects of stress on these synaptic changes are blocked by the glucocorticoid antagonist RU486, while the effects of cocaine on these changes are not (61). This suggests a role of synaptic plasticity at midbrain excitatory DA neurons induced by rewarding drugs as a possible mechanism underlying their reinforcing effects. Furthermore, serotonin, endogenous opioids, glutamate and y-aminobutyric acid (GABA), which all modulate the DA-dependent effects of psychostimulants (62), are themselves modulated by glucocorticoid pathways (63). Thus, increased levels of corticosterone secretion, or cortisol in humans, whether intrinsically or induced by stress, appears to up-regulate the DA system, acting to enhance the reinforcing effects of drugs and increase the vulnerability to addiction. Pharmacological manipulations of corticosterone secretion have been examined as therapeutic targets for the treatment of addiction. Rats placed under stress (food restriction) which showed higher corticosterone levels were injected with metyrapone, a corticosterone synthesis inhibitor, which abolished the increased locomotor response to cocaine – a hallmark of increased vulnerability to addiction – induced by stress, as compared to controls injected with saline (64). However, despite the apparent strength of these findings, there have been remarkably few recent studies following this avenue of research. 

As aforementioned, the consensus in the scientific literature is that the best way to treat addiction is to replace the addicts’ bond with the drug with healthy bonds, and to teach more effective ways of coping with stress – such as by the communication skills training approach (65,66). Recent research by Tops et al (2014) provides a model for the neurobiological basis of this, proposing that stress coping and social attachment share overlapping neurobiological systems (67). Responding to a novel stressor requires initial novelty processing and activation of learning mechanisms which enable habituation to the stressor. Similarly, forming attachments to novel individuals involves a similar process of novelty processing and habituation to their rewarding – or stressful – properties as the person becomes familiarised with the novel individual. The authors propose that “oxytocin facilitates social attachment and protects against addiction by stimulating the shift from novelty seeking to preference of familiarity”, and discuss findings in humans which support the notion that oxytocin facilitates this shift in part due to alterations in corticostriatal loops which mediate the derogation of alternatives. This serves to decrease novelty seeking and favour social attachment over the use of drugs as alternatives to healthy social connections. It is proposed that oxytocin exerts these effects in part via downstream modulation of dopaminergic, serotonergic and endogenous opioid systems – the latter two of which have been implicated in the drive to withdraw and seek pleasure (68). The authors conclude that this role of oxytocin supports the favouring of social bonds over drug use and serves to “increase resilience in the face of stress and addiction” (67)

Finally, orexins – a family of neuropeptides which regulate numerous processes including wakefulness (69), appetite (70) and mood (71) – are known to modulate the HPA axis in response to stressful stimuli (72). For example, Orexin-A stimulates the HPA axis resulting in an increased release of corticosterone and ACTH (73), thus implicating its importance in stress and addiction (74). Furthermore, dense orexinergic innervations are observed in many of the same brain regions as those expressing CRF, and many of these project to the ventral tegmental area and NAc (72), both brain regions comprising the mesolimbic reward pathway widely considered important in addiction (37). Thus, orexins are currently being studied in relation to the motivational drive for rewarding drugs such as psychostimulants (75), morphine (76,77), alcohol (78) and nicotine (79). Therefore, individual differences in the expression of orexins may further contribute to an individual’s vulnerability to addiction, through some of the same pathways that corticosterone/cortisol stimulate in response to stress. 

Thus, a growing body of research suggests that, while overuse of some rewarding drugs can result in physical dependency, it is not the drug itself which principally leads to addiction, but the substrate by which it acts – i.e. the innate biochemistry of the user, their environment, and the ways in which the individual responds to and copes with stress. Humans have an innate desire to form connections, and when their environment cannot offer that, they will bond with something which gives them a sense of relief – be it rewarding drugs, gambling, smartphones or pornography (2). Addiction treatment should therefore focus on correcting the individuals’ maladaptive responses to stress, providing social support and fostering a stress-free environment which favours healthy social bonds and healthy, productive connections – rather than social stigma, unemployability and incarceration.



References
1. Sachs KS. A Psychological Analysis of the 12 Steps of Alcoholics Anonymous. Alcoholism Treatment Quarterly. 2009 Apr 6;27(2):199–212.
2. Hari J. The Likely Cause of Addiction Has Been Discovered, and It Is Not What You Think [Internet]. Huffington Post. 2015 [cited 2017 Feb 28]. Available from: http://www.huffingtonpost.com/johann-hari/the-real-cause-of-addicti_b_6506936.html
3. A Brief History of Addictions Research and Treatment [Internet]. eLearnPortal. 2016 [cited 2017 Feb 28]. Available from: http://www.elearnportal.com/courses/psychology/psychology-of-addictions/psychology-of-addictions-a-brief-history-of-addictions-research-and-treatme
4. Weeks JR, Collins RJ. Factors affecting voluntary morphine intake in self-maintained addicted rats. Psychopharmacologia. 1964 Jul 1;6(4):267–79.
5. Dews PB. Studies on Behavior. Iv. Stimulant Actions of Methamphetamine. J Pharmacol Exp Ther. 1958 Jan 1;122(1):137–47.
6. Deneau G, Yanagita T, Seevers MH. Self-administration of psychoactive substances by the monkey. Psychopharmacologia. 1969 Jan 1;16(1):30–48.
7. Ahmed SH. Imbalance between drug and non-drug reward availability: A major risk factor for addiction. European Journal of Pharmacology. 2005 Dec 5;526(1–3):9–20.
8. Alexander BK, Beyerstein BL, Hadaway PF, Coambs RB. Effect of early and later colony housing on oral ingestion of morphine in rats. Pharmacology Biochemistry and Behavior. 1981 Oct;15(4):571–6.
9. Hadaway PF, Alexander BK, Coambs RB, Beyerstein B. The effect of housing and gender on preference for morphine-sucrose solutions in rats. Psychopharmacology. 1979;66(1):87–91.
10. Bozarth MA, Murray A, Wise RA. Influence of housing conditions on the acquisition of intravenous heroin and cocaine self-administration in rats. Pharmacology Biochemistry and Behavior. 1989 Aug;33(4):903–7.
11. Lenoir M, Serre F, Cantin L, Ahmed SH. Intense Sweetness Surpasses Cocaine Reward. PLOS ONE. 2007 Aug 1;2(8):e698.
12. Carroll ME, Lac ST, Nygaard SL. A concurrently available nondrug reinforcer prevents the acquisition or decreases the maintenance of cocaine-reinforced behavior. Psychopharmacology. 1989 Jan 1;97(1):23–9.
13. Huidobro F. Studies on morphine. VI. Ingestion of morphine solutions in normal mice and rats and in animals with chronic morphinism. Archives of International Pharmacodynamics. 1964 Oct 1;151:299–312.
14. Alexander BK, Hadaway PF. Opiate addiction: The case for an adaptive orientation. Psychological Bulletin. 1982;92(2):367–81.
15. Zinberg N, Jacobson R. The natural history of ‘chipping’. AJP. 1976 Jan 1;133(1):37–40.
16. Haney M, Maccari S, Le Moal M, Simon H, Vincenzo Piazza P. Social stress increases the acquisition of cocaine self-administration in male and female rats. Brain Research. 1995 Nov 6;698(1–2):46–52.
17. Shaham Y, Stewart J. Exposure to mild stress enhances the reinforcing efficacy of intravenous heroin self-administration in rats. Psychopharmacology. 1994 Apr 1;114(3):523–7.
18. Ames SC, Roitzsch JC. The impact of minor stressful life events and social support on cravings: A study of inpatients receiving treatment for substance dependence. Addictive Behaviors. 2000 Jul;25(4):539–47.
19. DeLongis A, Coyne JC, Dakof G, Folkman S, Lazarus RS. Relationship of daily hassles, uplifts, and major life events to health status. Health Psychology. 1982;1(2):119–36.
20. Monroe SM. Major and minor life events as predictors of psychological distress: Further issues and findings. J Behav Med. 1983 Jun 1;6(2):189–205.
21. Pickens RW, Hatsukami DK, Spicer JW, Svikis DS. Relapse by Alcohol Abusers. Alcoholism: Clinical and Experimental Research. 1985 May 1;9(3):244–7.
22. Hassanbeigi A, Askari J, Hassanbeigi D, Pourmovahed Z. The Relationship between Stress and Addiction. Procedia - Social and Behavioral Sciences. 2013 Jul 9;84:1333–40.
23. Measelle JR, Stice E, Springer DW. A prospective test of the negative affect model of substance abuse: Moderating effects of social support. Psychology of Addictive Behaviors. 2006;20(3):225–33.
24. Kassel JD, Greenstein JE, Evatt DP, Roesch LL, Veilleux JC, Wardle MC, et al. Chapter 8 - Negative Affect and Addiction. In: al’Absi M, editor. Stress and Addiction [Internet]. Burlington: Academic Press; 2007 [cited 2017 Mar 1]. p. 171–89. Available from: http://www.sciencedirect.com/science/article/pii/B9780123706324500115
25. Brewer DD, Catalano RF, Haggerty K, Gainey RR, Fleming CB. RESEARCH REPORT A meta-analysis of predictors of continued drug use during and after treatment for opiate addiction. Addiction. 1998 Jan 1;93(1):73–92.
26. Piazza PV, Le Moal M. Pathophysiological Basis of Vulnerability to Drug Abuse: Role of an Interaction Between Stress, Glucocorticoids, and Dopaminergic Neurons. Annual Review of Pharmacology and Toxicology. 1996;36(1):359–78.
27. Piazza PV, Deminèiere J-M, Maccari S, Mormède P, Moal ML, Simon H. Individual reactivity to novelty predicts probability of amphetamine self-administration. Behavioural Pharmacology [Internet]. 1990;1(4). Available from: http://journals.lww.com/behaviouralpharm/Fulltext/1990/00140/Individual_reactivity_to_novelty_predicts.7.aspx
28. Belin D. High Impulsivity Predicts the Switch to Compulsive Cocaine Taking. Science. 2008 Jun 6;320(5881):1352–1355.
29. Piazza PV, Maccari S, Deminière JM, Le Moal M, Mormède P, Simon H. Corticosterone levels determine individual vulnerability to amphetamine self-administration. Proceedings of the National Academy of Sciences. 1991 Mar 15;88(6):2088–92.
30. Deminière JM, Piazza PV, Guegan G, Abrous N, Maccari S, Moal ML, et al. Increased locomotor response to novelty and propensity to intravenous amphetamine self-administration in adult offspring of stressed mothers. Brain Research. 1992 Jul 17;586(1):135–9.
31. Raubenheimer PJ, Young EA, Andrew R, Seckl JR. The role of corticosterone in human hypothalamic-pituitary-adrenal axis feedback. Clin Endocrinol (Oxf). 2006 Jul;65(1):22–6.
32. Kosten TA, Miserendino MJD, Haile CN, DeCaprio JL, Jatlow PI, Nestler EJ. Acquisition and maintenance of intravenous cocaine self-administration in Lewis and Fischer inbred rat strains. Brain Research. 1997 Dec 19;778(2):418–29. 
33. Martı́n S, Manzanares J, Corchero J, Garcı́a-Lecumberri C, Crespo JA, Fuentes JA, et al. Differential basal proenkephalin gene expression in dorsal striatum and nucleus accumbens, and vulnerability to morphine self-administration in Fischer 344 and Lewis rats. Brain Research. 1999 Mar 13;821(2):350–5.
34. Dhabhar FS, McEwen BS, Spencer RL. Stress response, adrenal steroid receptor levels and corticosteroid-binding globulin levels — a comparison between Sprague-Dawley, Fischer 344 and Lewis rats. Brain Research. 1993 Jul 9;616(1–2):89–98.
35. Miller GM. The emerging role of trace amine-associated receptor 1 in the functional regulation of monoamine transporters and dopaminergic activity. Journal of Neurochemistry. 2011 Jan 1;116(2):164–76.
36. Cho AK, Melega WP, Kuczenski R, Segal DS, Schmitz DA. Caudate–putamen dopamine and stereotypy response profiles after intravenous and subcutaneous amphetamine. Synapse. 1999 Feb 1;31(2):125–33.
37. Robinson TE, Berridge KC. The neural basis of drug craving: An incentive-sensitization theory of addiction. Brain Research Reviews. 1993 Sep;18(3):247–91.
38. Hooks MS, Colvin AC, Juncos JL, Justice Jr. JB. Individual differences in basal and cocaine-stimulated extracellular dopamine in the nucleus accumbens using quantitative microdialysis. Brain Research. 1992 Aug 7;587(2):306–12.
39. Härfstrand A, Fuxe K, Cintra A, Agnati LF, Zini I, Wikström AC, et al. Glucocorticoid receptor immunoreactivity in monoaminergic neurons of rat brain. PNAS. 1986 Dec 1;83(24):9779–83.
40. Imperato A, Puglisi-Allegra S, Casolini P, Zocchi A, Angelucci L. Stress-induced enhancement of dopamine and acetylcholine release in limbic structures: role of corticosterone. European Journal of Pharmacology. 1989 Jun 20;165(2):337–8.
41. Rivet J-M, Stinus L, LeMoal M, Morme`de P. Behavioral sensitization to amphetamine is dependent on corticosteroid receptor activation. Brain Research. 1989 Sep 25;498(1):149–53.
42. Caggiula AR, Antelman SM, Aul E, Knopf S, Edwards DJ. Prior stress attenuates the analgesic response but sensitizes the corticosterone and cortical dopamine responses to stress 10 days later. Psychopharmacology. 1989;99(2):233–7.
43. Goeders NE, Guerin GF. Effects of surgical and pharmacological adrenalectomy on the initiation and maintenance of intravenous cocaine self-administration in rats. Brain Research. 1996 May 25;722(1–2):145–52.
44. Fahlke C, Hård E, Thomasson R, Engel JA, Hansen S. Metyrapone-induced suppression of corticosterone synthesis reduces ethanol consumption in high-preferring rats. Pharmacology Biochemistry and Behavior. 1994 Aug;48(4):977–81.
45. Deroche V, Piazza PV, Moal ML, Simon H. Social isolation-induced enhancement of the psychomotor effects of morphine depends on corticosterone secretion. Brain Research. 1994 Mar 21;640(1–2):136–9.
46. Richardson HN, Lee SY, O’Dell LE, Koob GF, Rivier CL. Alcohol self-administration acutely stimulates the hypothalamic-pituitary-adrenal axis, but alcohol dependence leads to a dampened neuroendocrine state. European Journal of Neuroscience. 2008 Oct 1;28(8):1641–53.
47. Barik J, Marti F, Morel C, Fernandez SP, Lanteri C, Godeheu G, et al. Chronic Stress Triggers Social Aversion via Glucocorticoid Receptor in Dopaminoceptive Neurons. Science. 2013 Jan 18;339(6117):332–5.
48. Stephens MAC, Wand G. Stress and the HPA Axis: Role of Glucocorticoids in Alcohol Dependence. Alcohol Research: Current Reviews. 2012;34(4):468–83.
49. Reuter M, Hennig J. Cortisol as an indicator of dopaminergic effects on nicotine craving. Human Psychopharmacology: Clinical and Experimental. 2003 Aug;18(6):437–46.
50. Hamidovic A, Childs E, Conrad M, King A, de Wit H. Stress-induced changes in mood and cortisol release predict mood effects of amphetamine. Drug and Alcohol Dependence. 2010 Jun 1;109(1–3):175–80.
51. Corominas M, Roncero C, Casas M. Corticotropin releasing factor and neuroplasticity in cocaine addiction. Life Sciences. 2010 Jan 2;86(1–2):1–9.
52. Bruijnzeel AW. Tobacco addiction and the dysregulation of brain stress systems. Neuroscience & Biobehavioral Reviews. 2012 May;36(5):1418–1441.
53. Lovallo WR. Cortisol secretion patterns in addiction and addiction risk. International Journal of Psychophysiology. 2006 Mar;59(3):195–202.
54. Steptoe A, Ussher M. Smoking, cortisol and nicotine. International Journal of Psychophysiology. 2006 Mar;59(3):228–35.
55. Barrot M, Rouge-Pont F, Maccari S, Marinelli M, Le Moal M, Piazza P. Glucocorticoids and drug abuse (III). Influences of basal corticosterone secretion on the effects of cocaine and morphine on accumbens dopamine. Society for Neuroscience Abstracts. 1994;20(2):666–7.
56. Markey KA, Towle AC, Sze PY. Glucocorticoid Influence on Tyrosine Hydroxylase Activity in Mouse Locus Coeruleus during Postnatal Development. Endocrinology. 1982 Nov 1;111(5):1519–23.
57. Ortiz J, DeCarpio JL, Kosten TA, Nestler EJ. Strain-selective effects of corticosterone on locomotor sensitization to cocaine and on levels of tyrosine hydroxylase and glucocorticoid receptor in the ventral tegmental area. Neuroscience. 1995 Jul;67(2):383–97.
58. Caesar PM, Collins GGS, Sandler M. Catecholamine metabolism and monoamine oxidase activity tn adrenalectomized rats. Biochemical Pharmacology. 1970 Mar 1;19(3):921–6.
59. Ho-Van-Hap A, Babineau LM, Berlinguet L. Hormonal action on monoamine oxidase activity in rats. Can J Biochem. 1967 Mar 1;45(3):355–62.
60. Gilad G, Rabey J, Gilad V. Presynaptic effects of glucocorticoids on dopaminergic and cholinergic synaptosomes: implications for rapid endocrine-neural interactions in stress. Life Sciences. 1987;40:2401–8.
61. Saal D, Dong Y, Bonci A, Malenka RC. Drugs of Abuse and Stress Trigger a Common Synaptic Adaptation in Dopamine Neurons. Neuron. 2003 Feb 20;37(4):577–82.
62. Le Moal M, Simon H. Mesocorticolimbic dopaminergic network: functional and regulatory roles. Physiol Rev. 1991 Jan 1;71(1):155.
63. Joëls M, Ronald de Kloet E. Mineralocorticoid and glucocorticoid receptors in the brain. Implications for ion permeability and transmitter systems. Progress in Neurobiology. 1994 May;43(1):1–36.
64. Marinelli M, Le Moal M, Piazza PV. Acute pharmacological blockade of corticosterone secretion reverses food restriction-induced sensitization of the locomotor response to cocaine. Brain Research. 1996 Jun 17;724(2):251–5.
65. Monti PM, Abrams DB, Binkoff JA, Zwick WR, Liepman MR, Nirenberg TD, et al. Communication skills training, communication skills training with family and cognitive behavioral mood management training for alcoholics. J Stud Alcohol. 1990 May 1;51(3):263–70.
66. Rohsenow DJ, Martin RA, Monti PM. Urge-specific and lifestyle coping strategies of cocaine abusers: Relationships to treatment outcomes. Drug and Alcohol Dependence. 2005 May 9;78(2):211–9.
67. Tops M, Koole SL, Jzermanb HI, Buisman-Pijlman FTA. Why social attachment and oxytocin protect against addiction and stress: Insights from the dynamics between ventral and dorsal corticostriatal systems. Pharmacology Biochemistry and Behavior. 2014 Apr;119:39–48.
68. Tops M, Russo S, Boksem MA., Tucker DM. Serotonin: Modulator of a drive to withdraw. Brain and Cognition. 2009 Dec;71(3):427–436.
69. Sherin JE, Elmquist JK, Torrealba F, Saper CB. Innervation of Histaminergic Tuberomammillary Neurons by GABAergic and Galaninergic Neurons in the Ventrolateral Preoptic Nucleus of the Rat. J Neurosci. 1998 Jun 15;18(12):4705.
70. Baird J-P, Choe A, Loveland JL, Beck J, Mahoney CE, Lord JS, et al. Orexin-A Hyperphagia: Hindbrain Participation in Consummatory Feeding Responses. Endocrinology. 2009 Mar 1;150(3):1202–16.
71. Blouin AM, Fried I, Wilson CL, Staba RJ, Behnke EJ, Lam HA, et al. Human hypocretin and melanin concentrating hormone levels are linked to emotion and social interaction. Nature communications. 2013;4:1547.
72. Srinivasan S, Shariff M, Bartlett S. The role of the glucocorticoids in developing resilience to stress and addiction. Frontiers in Psychiatry. 2013;4:68.
73. Kagerer S, Jöhren O. Interactions of orexins/hypocretins with adrenocortical functions. Acta Physiologica [Internet]. 2010 Mar;198(3). Available from: http://onlinelibrary.wiley.com/doi/10.1111/j.1748-1716.2009.02034.x/abstract
74. Pañeda C, Winsky-Sommerer R, Boutrel B, De Lecea L. The corticotropin-releasing factor-hypocretin connection: Implications in stress response and addiction. Drug News & Perspectives. 2005 Jun;18(4):250–5.
75. Borgland SL, Taha SA, Sarti F, Fields HL, Bonci A. Orexin A in the VTA Is Critical for the Induction of Synaptic Plasticity and Behavioral Sensitization to Cocaine. Neuron. 2006 Feb 16;49(4):589–601.
76. Georgescu D, Zachariou V, Barrot M, Mieda M, Willie JT, Eisch AJ, et al. Involvement of the Lateral Hypothalamic Peptide Orexin in Morphine Dependence and Withdrawal. J Neurosci. 2003 Apr 15;23(8):3106–11.
77. Sharf R, Guarnieri DJ, Taylor JR, DiLeone RJ. Orexin mediates morphine place preference, but not morphine-induced hyperactivity or sensitization. Brain Research. 2010 Mar 4;1317:24–32.
78. Jupp B, Krivdic B, Krstew E, Lawrence AJ. The orexin1 receptor antagonist SB-334867 dissociates the motivational properties of alcohol and sucrose in rats. Brain Research. 2011 May 19;1391:54–9.
79. von der Goltz C, Koopmann A, Dinter C, Richter A, Rockenbach C, Grosshans M, et al. Orexin and leptin are associated with nicotine craving: A link between smoking, appetite and reward. Psychoneuroendocrinology. 2010 May;35(4):570–7.

Read more »