NIH Scientists Identify a Potential New Treatment for Depression: A Metabolite of Ketamine

In a remarkable example of scientific collaboration, a new study produced by scientists at various research centers at the National Institutes of Health (NIH) have identified how ketamine works as powerful and fast-acting anti-depressant. This discovery may lead to an effective and potent new treatment for depression.

Ketamine is normally used as an anesthetic but at low doses, it has been shown to have rapid acting and long-lasting anti-depressant effects in humans. Fast relief of depression is incredibly important because most anti-depressant medications are not very effective or can take weeks (or even months in some cases) for maximal effect, which hurts the recovery of patients suffering from this crippling psychiatric disorder. However, despite its rapid action, ketamine has many side effects such as euphoria (a “high” feeling), dissociative effects (a type of hallucination involving a sense of detachment or separation from the environment and the self), and it is addictive.

If ketamine could be made safe to use without any of its other more dangerous properties, it would be a powerful anti-depressant medication.

With this goal in mind, scientists at the National Institute of Mental Health (NIMH), National Institute on Aging (NIA), National Center for Advancing Translational Sciences (NCATS), University of Maryland, and University of North Carolina-Chapel Hill sought to unravel the mystery of how ketamine works.

When ketamine enters the body it is broken down (metabolized) into many other chemical byproducts (metabolites). The team of scientists identified that it’s not ketamine itself but one of it’s metabolites, called HNK, that is responsible for ketamine’s anti-depressant action Most importantly HNK does not have any of the addictive or hallucinogenic properties of ketamine. What does this mean? This special metabolite can now be produced and can be given to patients while ketamine (and all its unwanted negative side effects) can be bypassed.

depressionOf course, many tests still need to be done in humans to confirm the effectiveness of HNK, but the study is an amazing example of how an observation can be made in the clinic, brought in the lab for detailed analysis, and then brought back to the clinic as a potential effective treatment.

But how did the scientist’s do it and how do they know that this HNK is what’s responsible for ketamine’s depression-fighting power? Keep reading below to find out.

Also, check out the NIH’s press release on the study.

The original study can be found here.

What is ketamine?

Chemical structure of ketamine (

Ketamine has traditionally been used an as anesthetic due to it’s pain relieving and consciousness-altering properties [1]. However, at doses too low to induce anesthesia, it has been shown that ketamine has the ability to relieve depression [2]. Even more remarkably, the anti-depressant effects of ketamine occur within a few hours and can last for a week with only a single dose. Most anti-depressant medications can take weeks before they start relieving the symptoms of depression (this is due to how those medications work in the brain).

However, ketamine also has unwanted psychoactive properties, which limits its usefulness in the treatment of depression. Ketamine causes an intense high or sense of euphoria as well as hallucinogenic effects such as dissociation, a bizarre sense of separation of the mind from the self and environment. Ketamine is also addictive and is an abused party drug [3].

A debate has been going about whether ketamine should be used for the treatment of depression and if its risks outweigh its benefits [4]. However, what if ketamine itself is not responsible for the anti-depressant function but a chemical byproduct of ketamine? This is what the scientist’s in this study reported: it’s HNK and not ketamine that are responsible for the powerful anti-depressant functions. This discovery was made in mice but how do scientists even study depression in a mouse?


How do scientists study depression in rodents?


Depression is a complex psychological state that is difficult to study but scientists have developed a number of tests to measure depressive-like behavior in rodents. While any one particular test is probably not good enough to measure depression, the combination of multiple tests—especially if similar results are found for each test—provide an accurate measurement of depression in rodents.

Some of the tests include:

Forced Swim Test

As the name reveals, in this test rodents are place in a cylinder of water in which they cannot escape are a forced to swim. Mice and rats are very good swimmers and when placed in the water will swim around for a while, searching for a way to escape. However, after a certain amount of time, the mouse will “give up” and simply stop swimming and will just float there. This “giving up” is used as a proxy for depression, similar to how people that are depressed often lack perseverance or motivation to keep trying. If you a give drug and the mice swim for much longer than without the drug, then you can make the argument that the drug had an anti-depressant effect. See this video of a Forced Swim Test.

Learned Helplessness Test

One theory of depression is that it can result from being placed in a bad situation in which we have no control over. This test models this type of scenario.

First, mice are place in chamber where they experience random foot shocks (the learning about the bad, hopeless situation). Next, they are place in a chamber that has two compartments. When a foot shock occurs, a door opens to a “safe” chamber, which gives the mouse an opportunity to escape the bad situation. One measure of depression is that some mice won’t try to escape or will fail to escape. In essence, they’ve given up at trying to escape the bad situation (learned helplessness). You can then take these “depressed” mice, and run the experiment again but this time with the anti-depressant drug you want to test and see how they do at escaping the foot shocks. Read more here.

Chronic Social Defeat Stress

Imagine you had a bully that would beat you up every day but the bully lived next door to you and would stare at you through his bedroom window? It would probably make you feel pretty crummy, wouldn’t it? Well, in essence, that’s what chronic social defeat stress test is all about [5].

A male mouse is placed in a cage with a much larger, older, and meaner male mouse that then attacks it. After the attack session, the “victim” mouse is housed in a cage where it can see and smell the bigger mouse. This induces a sense of hopelessness or depression in the “victim” mouse and it will not try to interact with a “stranger”” mouse if given a choice between the stranger and an empty cage (mice are pretty curious animals and will usually sniff around a cage with a unfamiliar mouse in it). This social avoidance is a measure of depression. In contrast, some mice will be resilient or resistant to this type of stress and will interact normally with the “stranger” mouse. Similar to above, you can test an anti-depressant drug in the “resilient” mice and the “depressed” mice.

There are a few others but these are three of the main ones used in this paper.

How did the NIH scientists figure out how Ketamine works to fight depression?

It was believed that ketamine’s anti-depressant function was due to its ability to inhibit the activity of the neurotransmitter glutamate. Specifically, ketamine inhibits a special target of glutamate called the NMDA receptor [6].

The first thing done is this paper was to study ketamine’s effects in rodent models of depression and sure enough, it was effective at relieving depression-like behavior in the mice.

Ketamine comes in two different chemical varieties or enantiomers, R-ketamine and S-ketamine. Interestingly, the R-version was more effective than the S-version (this will be more important later).

Recall that ketamine is though to work because it inhibits the NMDA receptor, but the scientists found that another drug, MK-801, that also directly inhibits the NMDA receptor, did have the same anti-depressant effects. So what is it about ketamine that makes it a useful anti-depressant then if not it’s ability to inhibit the NMDA receptor?

Ketamine is broken down into multiple different other chemical byproducts or metabolites once it enters the body. The scientists were able to isolate and measure these different metabolites from the brains of mice. For some reason one of the metabolites, (2S,6S;2R,6R)-hydroxynorketamine (HNK) was found to be three times higher in females compared to males. Ketamine was also more effective at relieving depression in female mice compared to male mice and the scientists wondered: could it be because of the difference in the levels of the ketamine metabolite HNK?

To test this, a chemically modified version of ketamine was produced that can’t be metabolized. Amazingly the ketamine that couldn’t be broken down did not have any anti-depressant effects. This finding strongly suggests that it’s really is one of the metabolites, and not ketamine itself, that’s responsible for the anti-depressant activity. The most likely candidate? The HNK compound that showed the unusual elevation in females vs males.

Similar to ketamine, HNK comes in two varieties, (2S,6S)-HNK and (2R,6R)-HNK. The scientists knew that the R-version of ketamine was more potent than the S-version so they wondered if the same was true for HNK. Sure enough, (2R,6R)-HNK was able to relieve depression in mice while the S-version did not. The scientists appeared to have identified the “magic ingredient” of ketamine’s depression-relieving power.

These experiments required a great deal of sophisticated and complex analytical chemistry. However, this is beyond my area of expertise so unfortunately cannot discuss it further.

So now the team had what they thought was the “magic ingredient” from ketamine for fighting depression. But could they support their behavior work with more detailed molecular analyses?

The next step was to look at the actual properties of neurons themselves and see if (2R,6R)-HNK changed their function in the short and long term. Using a series of sophisticated electrophysiology experiments in which the activity of individual neurons can be measured, the scientists found that glutamate signaling was indeed disrupted. However, it appeared that a different type of glutamate receptor was involved: the AMPA receptor, and not NMDA receptor. The scientists confirmed this with protein analysis; components of the AMPA receptor increased in concentration in the brain over time. These data suggest that it is alterations in glutamate-AMPA signaling that underlies the long-term effectiveness of HNK.

OK, so great! HNK reduces depression but does it still have all the other nasty side effects of ketamine? If it does, then it’s no better than ketamine itself.

For the final set of experiments, the scientists looked at the psychoactive and addictive properties of ketamine. Using a wide range of behavioral tests that I won’t go into the details of, 2R,6R)-HNK had a much lower profile of side effects than ketamine.

Finally, ketamine is an addictive substance that can and is abused illegally. A standard test of addiction in mouse models is self-administration (I’ve discussed this technique previously). Mouse readily self-administer ketamine, which indicates they want to take more and more of it, just like a human addict. However, rodent’s do not self-administer HNK! This means that HNK is not addictive like ketamine.

mental health

In conclusion, (2R,6R)-HNK appears to be extremely effective at relieving depression in humans, has less side-effects than ketamine, and is not effective. Sounds pretty good to me!

Next step: does HNK work in humans? To be continued….

Selected References

  1. Peltoniemi MA, et al. Ketamine: A Review of Clinical Pharmacokinetics and Pharmacodynamics in Anesthesia and Pain Therapy. Clinical pharmacokinetics. 2016.
  1. Newport DJ, et al. Ketamine and Other NMDA Antagonists: Early Clinical Trials and Possible Mechanisms in Depression. The American journal of psychiatry. 2015;172(10):950-66.
  1. Morgan CJ, et al. Ketamine use: a review. Addiction. 2012;107(1):27-38.
  1. Sanacora G, Schatzberg AF. Ketamine: promising path or false prophecy in the development of novel therapeutics for mood disorders? Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology. 2015;40(5):1307.
  1. Hollis F, Kabbaj M. Social defeat as an animal model for depression. ILAR journal / National Research Council, Institute of Laboratory Animal Resources. 2014;55(2):221-32.
  1. Abdallah CG, et al. Ketamine’s Mechanism of Action: A Path to Rapid-Acting Antidepressants. Depression and anxiety. 2016.


Important Considerations in Optogenetics Behavioral Experiments

Image credit NSF, Inbal Goshen, Karl Deisseroth.
Image credit NSF, Inbal Goshen, Karl Deisseroth.

The third and final part of my three part guest blog series on Optogenetics has been published on the Addgene blog. Addgene is a nonprofit organization dedicated to making it easier for scientists to share plasmids and I’m thrilled to be able to contribute to their blog! This post covers the running behavioral experiments utilizing optogenetics.

Check it out!


The Materials Science of Optogenetics Experiments


The second part of my three part guest blog series on Optogenetics has been published on the Addgene blog. Addgene is a nonprofit organization dedicated to making it easier for scientists to share plasmids and I’m thrilled to be able to contribute to their blog! This post covers the material science aspects of running optogenetic experiments.

Check it out!

Optogenetics on the Addgene Blog: Part 1


The first part of my three part guest blog series on Optogenetics has been published on the Addgene blog. Addgene is a nonprofit organization dedicated to making it easier for scientists to share plasmids and I’m thrilled to be able to contribute to their blog!

Check it out!

The Genetic Link Between Creativity and Psychiatric Disease


The biological sciences are in a golden era: the number of advanced technological tools available coupled with innovations in experimental design has led to an unprecedented and accelerating surge in knowledge (at least as far as the number of papers published is concerned). For the first time in history, we are beginning to ask questions in biology that were previously unanswerable.

No field demonstrates this better than genetics, the study of DNA and our genes. With the advent of high-throughput DNA sequencing, genetic information can be acquired literally from thousands of individuals and even more remarkably, can be analyzed in a meaningful way. Genomics, or the study of the complete set of an organism’s DNA or its genome, directly applies these advances to probe answers to questions that are literally thousands of years old.

A recent study, a collaborative effort from scientists in Iceland, the Netherlands, Sweden, the UK, and the US, is an example of power of genomics and to answer these elusive questions.

Power eet al. Nat. Neursci. 2015. Title

The scientists posed an intriguing question: if you are at risk for a psychiatric disorder, are you more likely to be creative? Is there a link between madness and creativity?

Self-portrait with bandaged ear. Vincent van Gogh, 1889. (
Self-portrait with bandaged ear. Vincent van Gogh, 1889. (

Aristotle himself once said, “no great genius was without a mixture of insanity” and indeed, the “mad genius” archetype has long pervaded our collective consciousness. But Vincent Van Gogh cutting off his own ear or Beethoven’s erratic fits of rage are compelling stories but can hardly be considered empirical, scientific evidence.

But numerous studies have provided some evidence that suggests a correlation between psychiatric disorders and creativity but never before has an analysis of this magnitude been performed.

Genome-wide association studies (GWAS) take advantage of not only the plethora of human DNA sequencing data but also the computational power to compare it all. Quite literally, the DNA of thousands of individuals is lined up and, using advance computer algorithms, is compared. This comparison helps to reveal if specific changes in DNA, or genetic variants, are more common in individuals with a certain trait. This analysis is especially useful in identifying genetic variants that may be responsible for highly complex diseases that may not be caused by only a single gene or single genetic variant, but are polygenic, or caused by many different genetic variants. Psychiatric diseases are polygenic, thus GWAS is useful in revealing important genetic information about them.

This video features Francis Collins, the former head of the Human Genome Project and current director of the National Institutes of Health (NIH), explaining GWAS studies. The video is 5 years old but the concept is still the same (there’s not many GWAS videos meant for a lay audience).

The authors used data from two huge analyses that previously performed GWAS on individuals with either bipolar disorder or schizophrenia compared to normal controls. Using these prior studies, the author’s generated a polygenic risk score for bipolar disorder and for schizophrenia. This means that based on these enormous data sets, they were able to identify genetic variants that would predict if a normal individual is more likely to develop bipolar disorder or schizophrenia. The author’s then tested their polygenic risk scores on 86,292 individuals from the general population of Iceland and success! The polygenic risk scores did associate with the occurrence of bipolar disorder or schizophrenia.

Next, the scientists tested for an association between the polygenic risk scores and creativity. Of course, creativity is a difficult thing to define scientifically. The authors explain, “a creative person is most often considered one who take novel approaches requiring cognitive processes that are different from prevailing modes of thought.” Translation: they define creativity as someone who often thinks outside the box.

In order to measure creativity, the authors defined creative individuals as “belonging to the national artistic societies of actors, dancers, musicians, and visual artists, and writers.”

The scientists found that the polygenic risk scores for bipolar disorder and schizophrenia each separately associated with creativity while five other types of professions were not associated with the risk scores. An individual at risk for bipolar disorder or schizophrenia is more likely to be in creative profession than someone in a non-creative profession.

 The authors then compared a number of other analyses to see if this effect was due to other factors such as number of years in school or having a university degree but this did not alter the associations with being in a creative field.

Finally, the same type of analysis was done with two other data sets: 18,452 individuals from the Netherlands and 8,893 individuals from Sweden. Creativity was assessed slightly differently. Once again creative profession was used but also data from a Creative Achievement Questionnaire (CAQ), which reported achievements in the creative fields described above, was available for a subset of the individuals.

Once again, the polygenic risk scores associated with being in a creative profession to a similar degree as the Icelandic data set; a similar association was found with the CAQ score.

The authors conclude that the risk for a psychiatric disorder is associated with creativity, which provides concrete scientific evidence for Aristotle’s observation all those years ago.

However, future analyses will have to broaden the definition of creativity beyond just narrowly defined “creative” professions. For example, the design of scientific experiments involves a great deal of creativity but is not considered a creative profession and is therefore not included in these analyses, and a similar argument could be made with other professions. Also, no information about which genetic variants are involved or what their function is was discussed.

Nevertheless, this exciting data is an example of the power that huge genomic data sets can have in answering fascinating questions about the genetic basis of human behavior and complex traits.

For further discussion, read the News and Views article, a scientific discussion of the paper, which talks about potential evolutionary mechanisms to explain these associations.

The Formation of New Memories in the Human Brain

Image of the structure of the mouse Hippocampus (Image courtesy of
Image of the structure of the mouse Hippocampus (Image courtesy of

How are new memories created?

This is a fascinating question in neuroscience and at the very core of what makes us human. After all, our entire concept of ourselves is defined by our memories and without them, are we even ourselves? This is a pretty lofty philosophical discussion… but today we’re only interested in the neuroscience of memory.

In specific, what happens to individual neurons in the human brain when a new memory is created and recalled?

Researchers at the University of California-Los Angeles performed a study in humans that has shed some light on this important question. Published recently in the journal Neuron, the novelty of the study involved recording how many times a neuron would fire during a specially designed memory test. In other words, the scientists were able to monitor what happened to individual neurons in a human being as a new memory was being created!

Title Ison et al. 2015

This article is open access (able to downloaded and distributed for free). The article can be found here or download the pdf.

Before I go into what the researchers found, let’s see how it was done.

The subjects in the study were patients being treated for epilepsy. As part of their clinical diagnosis, they had been implanted with an electrode, a tool used to measure neuronal activity or in other words, the electrode measures how often a neuron fires. The fact these patients already had an electrode inserted into the brain for clinical reasons made it convenient for the researchers to conduct this study.

Left Temporal Lobe (
Left Temporal Lobe (

The brain region in which the electrode was implanted is called the medial temporal lobe (MTL). The image to the right is of the left human temporal lobe. The medial region of the temporal lobe is located more towards the center of the brain.

Human Hippocampus (
Human Hippocampus (

One specific region of the MTL, the hippocampus, is believed to be the primary brain region where memories are “stored”. Specifically, previous studies in animals and humans have suggested that the MTL and hippocampus are very important to encoding episodic memory. Episodic memory involves memories about specific events or places. In this study, the example of episodic memory being used is remembering seeing a person at a particular place. Another example: the game Simon™ can be considered a test of your brain’s ability to rapidly create and recall short-term episodic memories!

Simon game memory

*Note: Episodic memory is considered one of the main bifurcations of declarative memory, or memories that can be consciously recalled. The other type of declarative memory is semantic memory, which are memories of non-physical/tangible things, like facts.

To test the episodic memory of remembering a person at a particular place, images were presented to the patients while the neurons were being recorded. There were 5 different tasks (all completed within 25-30min). See Figure 1 below from the paper.

Figure 1: Experimental Design
Figure 1: Experimental Design

First, a pre-screening was done in which the patients was shown many random images of people and places. The activity of multiple neurons was recorded and the data was quickly analyzed then 3-8 pairs of images were compiled. In each pair, 1 image was “preferred” or “P” image, meaning the neurons being recorded fired when the “P” image was shown. The second image was “non-preferred” or “NP” image, meaning the neurons did not respond to it when it was shown.

The first task is the “Screening” test. Each “P” and “NP” image was shown individually and the neurons response to each was recorded. As you would expect, the neuron would fire heavily to the “P” image and not very much to the “NP” image.

The second task was the “learning task” in which a composite image of each of the “P” and “NP” image pairs was made. The person in the “P” image was digitally extracted and placed in front of the landmark in the “NP” image. After the composite images were shown, the individual images were shown again.

For example, in one image pair for one patient, the “P” image was a member of the patient’s family while the “NP” image was the Eiffel Tower (for this example, see Figure 2). The composite image in the “learning” task was the family member in front of the Eiffel Tower. Another example of a “P” image was Clint Eastwood and the “NP” image was the Hollywood sign. The composite image would therefore be Clint Eastwood in front of the Hollywood sign. (However, in some image pairs the “P” image was a place and “NP” image was a person).

The third task was “assessing learning”. The image of just the person in the composite image was shown and the patient had to pick out the correct landmark he/she was paired with. For example, the picture of the family member was shown and the patient would have to pick out the Eiffel Tower image.

The fourth task was the “recall” task. The landmark image was shown and the patient had to remember and say the person it was paired with. For example, the Eiffel Tower was shown and the patient had to say the family member’s name.

Finally, the fifth task was a “re-screening” in which each individual image was shown again so the neuron’s activity could be compared to the Task 1, pre-learning.

The activity of multiple neurons were recorded for each image for each of the tasks. The data was then analyzed in number of different ways and the activity of different neurons was reported.

And what was found?

Figure 2: Response of Neruons in the Hippocampus from One Patient
Figure 2: Response of Neurons in the Hippocampus from a Patient

Let’s go back to the family member/Eiffel tower example. The researchers were able to show that a neuron in the hippocampus responded heavily to the picture of the family member (“P” image) but not to the Eiffel Tower (“NP” image). After showing the composite image, the neuron now responded to the Eiffel Tower too in addition to the family member! (The neuron also fired a comparable amount to the individual family member image as the composite image).

As you can see in Figure 2, each little red or blue line indicates when a neuron fired. For example, in Task 1 you can clearly see more firing (more lines) to the “P” image than the “NP” image. You can see that after Task 2, the neuron responds to either the “P” or “NP” image (especially obvious in the Task 5). The middle graph indicates the firing rate of the neurons to the “NP” image and it clearly shows increased firing rate of the neuron after learning (AL) compared to before learning (BL). It may look small, but the scientists calculated a 230% increase in firing rate of the neuron to “NP” image after the learning/memory task took place!

What does this mean? It means that a new episodic memory has been created and a single neuron is now firing in a new pattern in order to help encode the new memory!

This was confirmed the other way around too. In another patient, the “P” neuron was the White House and the “NP” image was beach volleyball player Kerry Walsh. The neuron that was being recorded fired a lot when the image of the White House was shown but not so much for the Kerri Walsh image. Then the composite image was shown and the learning/recall tasks were performed. The neuron was shown to fire to both the White House image AND the Kerry Walsh image! The neuron was responding to the new association memory that was created!

Keep in mind these are just two examples. The scientists actually recorded from ~600 neurons in several different brain regions besides the hippocampus but they only used about 50 of them that responded to visual presentation of the “P” image, either a person or a landmark (the identification of visually responsive neurons was crucial part of the experiment). Remarkably, when the firing rates of all these neurons was averaged before and after the memory/learning tasks, a similar finding to the above examples was found: the neuron now responded to the “NP” image after the composite was shown!

Many other statistical analyses of the data was done to prove this was not just a fluke of one or two neurons but was consistent observation amongst all the neurons studied but I won’t go into those details now.

But what’s going on here? Are the neurons that respond to the “P” stimulus now directly responding to the “NP” image or is more indirect, some other neuron is responding to the “NP” which in turn signals to the “P” neuron to increase in firing? The authors performed some interesting analyses that both of these mechanisms may apply but for different neurons.

Finally, were all the recorded neurons that were engaged in encoding the new episodic memory located in the hippocampus? The answer is no. Responsive neurons were identified in several brain regions besides the hippocampus including the entorhinal cortex and the amygdala. But most of the responsive cells were located within the parahippocampal cortex, a region of the cortex that surrounds the hippocampus, thus not surprising it is very involved in encoding a new memory.

In conclusion, the scientists were able to observe for the first time the creation of a new memory in the human brain at the level of a single neuron. This is an important development but such a detailed analysis has never before been done in humans and, most importantly, in real time. Meaning, the experiment was able to observe the actual inception of a new memory at the neuronal level.

However, one major limitation is that the activity of these neurons were not studied in the long term so it’s unknown if the rapid change in activity is a short-term response to the association of the two images or if it really represents a long-term memory. The authors acknowledge this limitation but the problem is really in the difficulty of doing such studies in humans. It’s not really ethical to leave an electrode in someone’s brain just so that you can test them every week!

But what does all of this mean? The authors do suggest that the work may help to resolve a debate that has been going in on the psychology field since the 40s. Do associations form gradually or rapidly? These results strongly suggest new neurons rapidly respond to encode the new memory formation.

But how will these results shape the neuroscience of memory? The answer is I don’t know and no one does. Thus is the rich tapestry of neuroscience, another thread weaved by the continuing work of scientists all over the world  in order to understand what it is that makes us human: our brains.

Morphine and Oxycodone Affect the Brain Differently

(Neurons. Image from Ana Milosevic, Rockefeller University)
(Neurons. Image from Ana Milosevic, Rockefeller University)

Why are some drugs of abuse more addictive than others?

 This is a central question to the addiction field yet it remains largely a mystery. All drugs of abuse have a similar effect on the brain: they all result in increased amounts of the neurotransmitter dopamine (DA) in an important brain region called the mesolimbic pathway (also known as the reward pathway). One of the core components of this pathway is the ventral tegmental area (VTA), which contains many neurons that make and release DA. VTA neurons communicate with neurons in the nucleus accumbens (NAc). This means that the axons of VTA neurons project to and synapse on NAc neurons. When VTA neurons are stimulated, they release DA onto the NAc, and this is a core component of how the brain perceives that something is pleasurable or “feels good.” Many types of pleasurable stimuli (food, sex, drugs, etc.) cause DA to be released from the VTA onto the NAc (See the yellow box in the diagram below). In fact, all drugs of abuse cause this release of DA from VTA neurons onto NAc neurons.

*Important note: many other brain regions are involved in how the brain perceives the pleasurable feelings of drugs besides the VTA and NAc, but these regions represent the core of the pathway.

"Dopamineseratonin". Licensed under Public Domain via Wikipedia.
“Dopamineseratonin”. Licensed under Public Domain via Wikipedia.

Check out these videos for a more detailed discussion of the mesolimbic pathway.

But if all drugs of abuse cause DA release, then why do different drugs make you feel differently? This is a very complicated question but one component of the answer is that different drugs have different mechanisms and dynamics of DA release.

For the opioid drugs like heroin, morphine, and oxycodone, they are able to bind to a special molecule called the Mu Opioid Receptor (MOPR). This action on the MOPR results in an indirect activation of DA neurons in the VTA and a release of DA in the NAc. While all opioid drugs reduce the feeling of pain and induce a pleasurable feeling, they have slightly different properties at the MOPR.

The different properties of the opioids may be a reason why some are more abused than others. For example, a number of studies have suggested that oxycodone may have greater abuse potential than morphine. This means that oxycodone is more likely to be abused morphine.

But do the different properties of morphine and oxycodone on the MOPR affect DA release and is this important to why oxycodone is more likely to be abused than morphine?

Vander Weele et al. 2015 titleThis is the question that scientists at the University of Michigan sought to address. Using several different sophisticated techniques, the scientists looked at differences in DA release in the NAc caused by morphine and oxycodone, two common opioid drugs.

Rats were injected with either morphine or oxycodone and then DA release was measured using either fast-scan cyclic voltammetry or microdialysis. I’ve discussed microdialysis in a previous post but in brief, it involves drawing fluid from a particular brain region at different time points in an experiment and then measuring the neurotransmitters present (using advanced chemistry tools that I won’t explain here).

Voltammetry is a more technically complicated technique. In brief, it uses electrodes to measure sensitive voltage changes. Since a molecule has specific electrochemical properties, these voltage changes can be related back to a specific molecule, such as the neurotransmitter DA as in this study. Voltammetry may even allow greater temporal resolution (easier to detect very precise changes at very short time frames, like seconds), which may make it more accurate than microdialysis (which can only measure neurotransmitter release on the scale of minutes).

Because each technology has its own limitations and potential problems, the authors used both of these techniques to show that they are observing the same changes regardless of the technology being used. Showing the same observation multiple times but in different ways is a common practice in scientific papers: it increases your confidence that your experiment is actually working and what you are observing is real and not just some random fluke.

The authors administered a single dose of either morphine or oxycodone to rats and then measured the DA release in the NAc as described above. What they found were very different patterns!

Morphine resulted in a rapid increase in DA (less than 30 seconds) but by 60 seconds had returned to normal. In contrast, oxycodone took longer to rise (about 20-30 sec before a significant increase was detected) but remained high for the entire 2 minutes that it was measured. The difference in DA release caused by morphine and oxycodone is striking!

Many other changes were observed such as differences in DA release in different sub-regions of the NAc, different effects on phasic release of DA (DA is often released in bursts), and differences in the other neurotransmitters such as GABA (morphine caused an increase in GABA release too while oxycodone did not). I won’t discuss these details here but check out the paper for more details.

Of course, do these differences in DA release explain why oxycodone is more often abused than morphine? Unfortunately no, there are many other factors (for example, oxycodone is more widely available than morphine) to consider. Nevertheless, this is some intriguing neuroscientific evidence that adds one more piece to the addiction puzzle.

Childhood Abuse Has Long-lasting Effects on Brain Function

(© Derek Simon 2015)
(© Derek Simon 2015)


Why is it that one person becomes an addict and another does not?

This is a central question in addiction field and one that I’ve touched on in some of my posts (and will continue to explore in the future). Two recent papers may help to shed more light on this difficult and complicated question. Both studies have revealed changes that occur in the brain as a result of childhood trauma that may cause an individual to be more susceptible to risky behavior such as drug abuse.

Both papers are neuroimaging studies meaning they use living human subjects and look at brain activity in response to different scenarios. There are many ways to image a living brain but these studies both use functional magnetic resonance imaging (fMRI). Basically, fMRI measures blood flow into the brain. As neurons turn “on” (that is, when they conduct an electrical signal), they require energy. Neurons use glucose as their primary energy source, which is delivered to them through blood flow. Therefore, the more blood flowing to a region of the brain = the more energy required by neurons = more neurons “firing”.

 The analysis of fMRI data is very complicated and beyond the scope of my knowledge or this discussion. But in essence, when you think or read about something, certain areas of your brain process that information. Using fMRI, you can actually visualize regions of the brain that are turning “on” or “off” when a patient thinks about a particular situation! Watch these YouTube videos for additional explanations on fMRI.


fMRI Image (
fMRI Image (

In both of the studies featured in today’s post, subjects would read different scripts while in the fMRI scanner and the scientists would image the entire brain and identify the regions that were active during the test. Then data from multiple subjects can be compiled and a composite image that represents the averages all the subjects can be produced. The picture to the right is an example of this type of composite image. Finally, you can see which regions of the brain are active for most of the patients during the different experiments. Keep this information in mind as I go over the papers.

Elsey et al. Neuropsychopharm. 2015

The first paper performed fMRI scans on adolescents that had or had not experienced maltreatment or trauma during childhood (less than 18 years old). 67 subjects were recruited from a larger study looking at disadvantaged youth and 64 were eventually used in the study. The adolescents filled out a standard survey that allowed the scientists to learn which of the subjects had experienced maltreatment/trauma during childhood.

The experiment involved having the different subjects read a script about either a stressful moment, their favorite food, or something neutral or relaxing while their brains were being imaged in the fMRI scanner.

Amazingly, for the stressful scenario, a difference in brain activity was detected in multiple regions of the prefrontal cortex only in subjects that had experienced childhood maltreatment! What this means is those youths that were abused as kids responded to stress differently than youths that were not abused. Their brain function has literally been changed later in life as a result of the abuse they suffered as children.

 The prefrontal cortex is a part of the mesocorticolimbic system, a group of brain areas especially involved in addiction. The prefrontal cortex is also involved in decision making, impulsivity, and other functions. It’s not clear what this change in prefrontal cortex activity actually means but it is possible that the altered activity could make the youth more vulnerable to stress or more likely to engage in risky activities, such as drug abuse.

 Elton et al. Addiction Biol. 2014

The second study was also interested in subjects that had experienced maltreatment or trauma during childhood but it instead of adolescents, this study used subjects that are adult men dependent on cocaine. Similarly, the subjects were grouped into those that had been mistreated as kids and those that had not.

In a parallel design to the other study, the subjects read a script describing a situation while being scanned in the fMRI machine. The scripts in this study included stress, cocaine-associated, and neutral. Interestingly, an increase in activity in a specific region of the prefrontal cortex and an area of the brain involved in motor activity were detected in the subjects that had been abused during childhood. And even more important, these changes were correlated to enhanced drug craving. These results suggest that childhood trauma can affect drug craving for addicts, which may be relevant factor in triggering relapse. That is to say, addicts that have been abused as children may be more vulnerable to not only acquiring addiction but also relapse.

 It is important to keep in mind that, like the previous study, the real functional importance of these different changes in unknown. However, clearly there are real changes that occur in the brain as a result of abuse/maltreatment during childhood. Imaging data must be taken with a grain of salt because it is difficult to show real causality. Yet, both studies (and many others) suggest long-lasting changes in brain activity, especially in response to stress, as a result of childhood trauma/maltreatment.

The conclusions we can draw from these studies is that childhood mistreatment, or trauma can have lasting changes on the brain. How these changes affect behavior is a much more difficult question to answer. Nevertheless, the changes that occur may be one of the factors that can contribute to susceptibility to addiction. These studies are supported by a previous post in which animal studies have shown that stress during early age leads to greater drug use as an adult.

And a broader point, these two neuroimaging studies help to put a different perspective on disadvantaged youth and importance of a stable home life, the lack of which can significantly affect you as an adult and may even contribute to susceptibility of become a drug addict.

Optogenetics in the New Yorker

Optogenetics1Excellent new article on optogenetics in The New Yorker. Optogenetics is a powerful, cutting-edge tool developed by Karl Deisseroth’s lab (profiled in the article) and is one of most significant advances in neuroscience research in decades. I recently spent two months learning the technique and we will be implementing it in the lab I work in at Rockefeller University. Optogenetics allows researchers to turn specific neurons “on” and “off” and see how those neurons are directly involved in a particular behavior. The article does a great job of profiling Deisseroth himself and explaining a little bit of the history of optogenetics and other developments in the Deisseroth lab. Enjoy!



The Scientist’s Toolbox: Techniques in Addiction Research, Part 2

Lab Mice IMG_4102

When a news article starts with the headline “A new study finds…” do you know what that means? The article is (allegedly) referring to a peer-reviewed scientific research paper. Research papers are the heart of the scientific research field and are a report of a series of experiments conducted by a scientist or team of scientists. In a future post, I’ll do a break down what a paper looks like but for now all you need to know is that the heart of the paper is the data. The data are the pieces of information that scientists have acquired from their experiments and are reporting in the scientific paper.

But how do scientists generate data?

This is one of the crucial questions in the scientific field because it refers to experimental design: 1) what is the question the scientists wishes to answer, 2) which experiments does the scientist need to design in order to answer those questions and 3) what are the different techniques and tools needed in those experiments?

This is my second post in series of posts I’m doing to show how scientists actually collect data and the various experimental techniques and tools we have at our disposal. Right now I’m only talking about neuroscience and techniques specific to the addiction field but may discuss more general biological tools and experimental techniques in the future.

In my last post in this series, I discussed the locomotor activity test (also known as the open field test), intravenous self-administration, and microdialysis. Today, I’ll discuss a behavioral technique that’s an alternative to self-administration: conditioned place preference.

 Conditioned Place Preference

Recall our discussion on self-administration. It’s a powerful technique that allows animals to administer drugs to themselves. The technique also has the potential to model initiation of drug taking, maintenance/escalation in drug taking, and even relapse-like behaviors. However, there is one major flaw with this technique. It is extremely difficult and very time consuming! After all, a mouse jugular vein is really small, which makes doing the surgeries not a trivial exercise…

Is there an easier way to study addiction that doesn’t require surgery? Thankfully, there is! Conditioned place preference (CPP) is another model to test whether animals find a drug of abuse pleasurable/rewarding or not pleasurable/aversive.

The technique is based on a Pavlovian or classical conditioning mechanism. Perhaps you’ve heard of the famous Russian scientist Ivan Pavlov? In a series of very famous experiments, he was able to cause dogs to salivate anytime he rang a bell (or any neutral stimulus for that matter). Like most famous discoveries, he wasn’t trying to do this but through careful observations he uncovered one of the basic mechanisms that underlies learning.

Figure 1: Classical Conditioning (
Figure 1: Classical Conditioning

Pavlov’s conditioning experiment was done by presenting the dogs with an unconditioned stimulus, that is to say something that will cause a response in the animal no matter what, which is called an unconditioned response. In Pavlov’s case, he would present the dog with the unconditioned stimulus of food, which would cause the unconditioned response of salivating (Figure 1). Through careful observation, he was able to identify that dogs would salivate even before he put the food in front of them, sometimes just the site of the food dish was enough to cause the dogs to salivate. He followed up on this intriguing observation.

While the food is the unconditioned stimulus, the food dish or scientist bringing the food served as a neutral stimulus that normally would have no effect on the dogs ability to salivate. Pavlov tested if he could induce this salivating effect with other neutral stimuli. A neutral stimulus that normally has no effect on the animal, called a conditioned stimulus, would become associated with the unconditioned stimulus to produce a response (the conditioned response). In Pavlov’s experiments, the conditioned stimulus (food), when paired with the unconditioned stimulus (bell), would then produce a conditioned response (salivating).

Now let’s see how Pavlov’s conditioning experiment was actually done. If he rang the bell before the conditioning (the conditioned stimulus), it would have no effect. The dogs don’t really care about the noise from the bell because it is not associated with anything in the dog’s brains. But every time the food (unconditioned stimulus) is presented to the dogs, Pavlov would ring the bell (conditioned stimulus). Now the ringing of the bell became associated in the dog’s brain with the presence of the food.

Finally, after the conditioning sessions, Pavlov would ring the bell and would remarkably cause the dogs to salivate (conditioned stimulus)! They had learned to associate the sound of the bell with the presence of the food. Just to clarify, the dogs are not “choosing” to associate the bell with food. This type of conditioning is hard wired into the brain itself—forming these type of associations is one of the things that brain does best. In fact, classical conditioning is a basic mechanism in many types of learning. To this day, Pavlov’s work remains some of the foundational experiments in the biological basis of learning.

Here’s a video I found on YouTube that summarizes everything that you just read:


The taking of a psychoactive drug can actually have a similar type of classical conditioning effect. Think about it this way, a drug is never taken in a vacuum,it is always taken in a particular context. A drug may be frequently taken in a particular location, or under particular circumstances, or even with certain people.

I’m a former cigarette smoker and this is an example of conditioning that I personally experienced. Every time I got in the car I would light up a cigarette. After months and years of smoking, I caused a classical condition effect in myself. The cigarette (unconditioned stimulus) produces that “nicotine high” and relaxing feeling that smokers crave (unconditioned response). However, driving in a car (conditioned stimulu) normally does not cause that feeling. But every time I would need to drive someplace I would smoke. Eventually, simply being in the car would cause a craving for a cigarette! The conditioned stimulus of driving became associated with the unconditioned stimulus of smoking to produce the conditioned response of nicotine craving every time I go into the car.

Classical conditioning is exactly how conditioned place preference works. In the laboratory, we can use this basic mechanism to force mice to experience a conditioned response when placed in a distinctive chamber. The mouse will even seek out that chamber and spend time in it because they know that they received a “good feeling” anytime they were in the chamber before.

This is what the chamber looks like.

CPP Chamber (© Derek Simon 2014)
CPP Apparatus (© Derek Simon 2015)

It consists of three connected boxes: a central grey one with normal flooring, a white-walled one on the left with a mesh grating as the floor, and a black-walled one on the right with steel bars on the floor. There are special trap doors (white knobs in the picture below) that can be opened or closed so that a mouse is allowed to either explore the whole apparatus or be confined to one of the chambers. When the mouse is being conditioned, the trap doors are closed and the mouse stays in only one chamber the whole time.

CPP Apparatus (© Derek Simon 2014)
CPP Apparatus (© Derek Simon 2015)

A CPP experiment consists of four main steps: 1) the pre-test day, 2) the conditioning sessions (multiple of these), 3) the post-test day, and 4) data analysis. (See Figure 2 below).

Step 1: A mouse in placed in the central grey chamber and it is allowed to explore the entire apparatus all it wants want. Both the white and black chambers represent a conditioned stimulus because right now, they have no association with anything in the mouse’s brain. The time spent in each chamber is recorded.

Step 2: Now the mouse receives an injection of drug (or saline as a control substance) and then is placed in either the white or black chamber. The mouse is forced to stay in the chamber for the entire session (usually 15-30min). That way the features of the chamber (wall color and floor texture) become associated the unconditioned stimulus of the drug. One conditioning session occurs a day for several days.

Step 3: The test day. Now the trap doors are raised and the animal is allowed to explore all three chambers again. If the experiment worked, the mouse will spend most of its time in the chamber that it received the drug injections! In other words, the mouse was conditioned to expect the drug in either the white or black chamber and, given the choice, prefers to spend time in that chamber in anticipation of the drug.

Step 4: Analysis. The time spent in the drug or saline-paired chamber on the test day is subtracted from the time spent in that chamber on the pre-test day. This difference in time is considered the quantitative measure of a successful conditioning session.

This figure summarizes a CPP experiment:

Figure 2: Diagram of a CPP Experiment (© Derek Simon 2015)
Figure 2: Diagram of a CPP Experiment (© Derek Simon 2015)

If an animal likes a drug and finds it pleasurable and rewarding, it will spend a lot of time in the conditioning chamber (see the graph). If the mouse hates the drug, it will not spend time in the conditioning chamber. By using this setup, we can test how different drugs and doses of drugs, and other types of experimental manipulations can effect how a the mouse perceives the drug.

If we were to compare self-administration to CPP, a conditioned response in the CPP experiment would be a similar measure as self-administration of a drug. Both experiments reveal that the animal likes the drug and wants to take it.

And as with self-administration, many variations on the basic setup exist but I’ll spare you those details for now…

Thanks for reading!