10/31 Moore et al. and Kellendock et al.

            Moore et al. administered MAM to pregnant rat dams. This model exhibited a neuropathology similar to that of schizophrenia, along with cognitive inflexibility and sensorimotor gating deficits, when the MAM was administered on E17. Previously, most work done with a MAM model of schizophrenia involved administering the drug on E15. They believe that the E17 model is better model of schizophrenia since there are less unrelated side effects, like microcephaly and motor impairments, when it is administered on E17 rather than E15.

I was curious about how E17 was chosen. I would be curious to see the results of their tests on other days, like E16 or E18. MAM-E17 brains were still about 7% smaller than control brains, so maybe administering it on E18 would have less of an effect on overall brain size.

            In the reversal learning task, the methods stated that females were used as controls. It didn’t seem like there were females used at any other time in the paper, so this may affect the results of the task. I would also like to see if they get the same results from using the MAM-E17 model on females. Also, the authors are unsure of how MAM preferentially affects cortical neurons. If more were known about how this model works, the model would be more convincing to me.

            Kellendock et al. used a transgenic mouse model of schizophrenia that overexpressed D2 receptors using a tetracycline transactivator (tTA). This expression was limited to the striatum. Overexpression of DA did not cause more locomotor activity, deficits in sensorimotor gating, or increased anxiety compared to control mice. In an attentional set-shifting task, DA overexpression mice had increased latency to choose between odors during reversal trials. This corresponds to executive function impairment in schizophrenia. The transgenic mice were tested in a DNMTS maze task off and on dox. The cognitive deficits remained even when the mice were put on dox so the DA receptor overexpression gene was turned off. The authors also found increased D1 expression in the mPFC, which is involved in executive function. They believe that the cognitive deficits are due to an imbalance in D1 activation in the mPFC. There is evidence that striatal dysfunction affects the PFC, so more research could be done on this pathway in the future. Since there are differences in circuits between rodents and humans, it is difficult for the rodent research to be translational. However, rodent models can still be used to test single aspects of schizophrenia. In the future, more research could be done on the additional cognitive deficits of schizophrenia.

Schizophrenia - Halloween

Schizophrenia is an extremely complex disease of the brain, which is discussed in both Moore et al. and Kellendonk et al. Due to the various different brain structures involved in this disorder, it takes a lot of research and experiments to show just how complex it really is. Moore et al. tested for ataxia, PPI, orofacial dyskinesias and ambulation following amphetamine distribution, among other things. While their results support the schizophrenia model, they do not make it very clear on why they included the amphetamine experiment in their schizophrenia paper, unless I am missing something. One particular overlap I noticed between the two papers was the PPI experiment, although they used different variables which yielded different results. The Moore et al. experiment used MAM E17 (useful for schizophrenia models) to show that there is a decrease in prepulse inhibition of startle in that experimental group, whereas the Kellendonk et al. experiment showed that mice overexpressing D2 receptors (an increased activity of D2Rs has been linked to schizophrenia) showed no deficits in prepulse inhibition of startle. I found these results to be interesting, and most likely demonstrate the complexity and selectivity of schizophrenia.

            I think these rodent models are relatively effective in studying schizophrenia due to obvious ethical reasons and overall difficulty in studying it in humans, but just how much of these animal models can we translate to humans? Schizophrenia involves a vast number of different symptoms such as hallucinations and delusions, which are not something researchers can study in animal models. I think these papers both do a good job supporting the schizophrenia model, but Kellendonk et al. does a more believable approach as they manipulate the D2 receptor, which is how many antipsychotics work.

Complexities in Modeling Schizophrenia

To begin, I want to talk about the two theories/ hypotheses behind the etiology of schizophrenia. Schizophrenia symptoms are typically categorized into three categories as follows: Positive (hallucinations, delusions, disordered thoughts), Negative (flat affect, reduced pleasure in activities, reduced speaking), and Cognitive (poor executive functioning, problems with working memory, trouble focusing). The two main theories of schizophrenia focus on the malfunctioning of two different neurotransmitter systems to explain the various symptoms implicated in schizophrenia. 

The Dopamine Hypothesis came into view in the 1970s after scientists observed that the psychosis caused by amphetamine drugs was similar to the positive symptoms of schizophrenia. These drugs are dopamine receptor (D2) agonists. A class of drugs now known as anti-psychotics were found to be helpful in relieving positive symptoms by taking a reverse action and antagonizing D2 receptors. Unfortunately, these drugs do almost nothing to alleviate the negative symptoms of schizophrenia, implying that the etiology of the disease is much more complicated than an increase of dopamine activity. 

The Glutamate Hypothesis emerged in the 1980s after scientists observed that the effects of NDMA antagonist drugs like PCP and ketamine can be similar to the symptoms of schizophrenia. It is possible that the activity at this receptor can cause a wider variety of schizophrenia symptoms via interactions with dopamine and GABA. So far, there has been mixed results in new treatments that involve an NDMA agonist.

  I found the difference in approach between the two papers to be particularly interesting this week since one paper embraced both the dopamine and glutamate hypotheses and the other paper focused on the dopamine hypothesis alone. 

The Moore et al paper seeks to create a model of schizophrenia by disrupting brain development at an important time point to create reduced brain volumes in areas specific to schizophrenia. Although this model seems viable, it requires much further testing from this initial study. Moore et al has sufficiently shown that this model behaviorally looks like schizophrenia, but they have done minimal investigation about the model's effects on neurotransmitter activity. They do include some experiments with both PCP and amphetamine to show that MAM E17 rats have and elevated response to both drugs. This implies that their model has developmentally altered the glutamate and dopamine pathways, however, more exploration is needed in this area. They could have stained for TH (a enzyme vital to making dopamine) or NDMA receptors to examine the specific alterations that MAM causes over the course of development to see if these changes are analogous to changes seen in post mortem brains of humans with schizophrenia.

Alternatively, the Kellendonk et al paper focused on a model that only concerned the dopamine hypothesis and transgenically altered the expression of D2 receptors in the striatum. This paper outlines some of the major issues that come with models of schizophrenia that only focus on the dopamine hypothesis. This model does demonstrate that a developmental change in D2 expression causes long-lasting changes in cognitive function, even after the restoration of normal D2 expression. It would be interesting to see if this developmental change has significant effects on other neurotransmitter pathways- primarily glutamate and NDMA receptors. 

Regardless of the approach to modeling schizophrenia in animals, great care must be taken to avoid over-anthropomorphizing lab animals. It can become very easy to interpret animal behavior as an emotionally analogous to humans. However, it is important to remember that despite many genetic similarities, humans are still very different from rodents. It is impossible to ask mice and rats if they are hallucinating or having delusions of grandeur, or even to know if rodents can experience such things. Therefore any models that are created will have to be researched with a grain of salt.

Moore et al. vs Kellendonk et al. - Joe

Moore et al vs Kellendonk et al

Seminar in BioPsych

Fall 2016

Professor Shansky

Schizophrenia is a notoriously difficult mental illness to model, particularly because it it epigenetic and polygenic. Moore et al sought to create a reliable model for this disease in the rodent by carefully administering methylazoxymethanol acetate (MAM) at different stages in embryonic development. It had previously been shown that MAM administration on E15 led to development of a considerably smaller head; however, that is not typical of schizophrenic patients. The goal is to create a model that exhibits the anatomical, functional, and behavioral deficits of typical schizophrenic patients, and so Moore et al thought to try administering MAM two days later, on E17, when most proliferation of neurons has peaked in the cortex, so development would not be affected as strongly.

Fortunately, MAM-E17 mice did not exhibit the microcephaly that MAM-E15 mice did; their heads were similar in size to control mice. Moreover, brain region morphologies were similar to controls, aside from a slight reduction in cortical size. Very interestingly, they noticed that overall neuronal count was not affected; however, a reduction in density of neurons in one part of the brain was coupled with an increased density of neurons in another — to maintain the overall neuronal count. They tested a bunch of other measures to compare these mice to controls: electrophysiological recordings which showed that resting potentials in MAM-E17 mice were more depolarized than controls, but, interestingly, those same neurons were not more easily excited; behavioral assays which showed that experimental mice did not show gross motor abnormality, but did show a deficit in prepulse inhibition; pharmacological experiments which showed that MAM-E17 mice were more sensitive to increased schizophrenic stereotypies after PCP administration; and lastly, another pharmacological experiment which manifested a dichotomous responsiveness to amphetamine in prepubertal versus adult mice.

I’ve thought about what it would take to create a reliable model for a disease, or even just to create a transgenic mouse that behaves “normally” and I would imagine that it would require a plethora of intricate experiments with potentially shaky interpretations, because what does it really mean for a mouse to have schizophrenia — the florid symptoms can never be verified. Thus, I’m not sure I’m content with this series of experiments — not because of unruly data representation, but because I’m not sure how well a mouse model can truly represent the animal correlate of a schizophrenic patient. (It’s also interesting to note that it isn’t known how MAM methylation is preferentially targeted in cortical neurons.) Maybe there’s a more modest approach that may be more telling.

That brings us to Kellendonk et al, which was published just a month after Moore et al, and both papers came out of Columbia University. These experimenters thought: D2 receptor dysfunction has been implicated in schizophrenia in humans — specifically in the striatum —, and this dysfunction has a behavioral effect on working memory, which is known to be carried out largely by the prefrontal cortex (PFC). Thus, they thought, does overexpression of D2Rs in the striatum lead to cognitive deficits, and then, downstream, how is the PFC affected?

They adopted a tet-tag strategy to overexpress D2Rs in the striatum of transgenic mice. These mice were kept off dox to maintain overexpression; however, some of the experiments showed that introducing dox to return D2R expression back to normal did not reduce behavioral symptomatology — in fact, they may have exacerbated it. The first few experiments served to show that this mouse does indeed show dopamine activity level changes that parallel those of schizophrenic patients, and the next few experiments showed that locomotion, sensorimotor gating, and generalized anxiety levels were not vastly different from control animals. Then, the big experiment showed that these mice indeed showed a deficit in working memory, which is what they were hoping. So their next question was: what’s going on downstream of the striatum to lead to this behavioral deficit? They showed that lesioning the PFC led to a similar decline in ability to complete the working memory task, and this paralleled the decline in D2R transgenic mice. So the PFC is probably somehow involved in this deficit, but how? Well, DA innervation to the PFC seemed normal, as evidenced by density of TH-positive axon terminals in the PFC of experimental vs control mice. But, the levels of dopamine compared to its metabolites showed that dopamine was not getting biotransformed normally. Furthermore, administration of a selective agonist of excitatory DA receptors (D1R and D5R) showed a much stronger cfos activation in the PFC, but when the transgene was turned off, this cfos activation was much weaker! So overexpression of D2R probably has something to do with this downstream effect.

This approach to finding a neurobiological correlate of schizophrenia was much more modest and, in my opinion, more successful, than that of Moore et al. Instead of administering a drug whose drug action is not entirely understood and attempting to run after all the possible relations to human schizophrenia that it may have, Kellendonk et al took a much more targeted and controlled approach, by looking at DA receptor modulation, particularly because many schizophrenia treatments are based on DA modulation. I don’t know very much (anything) about schizophrenia, but I agree much more with the approach of Kellendonk et al than with that of Moore et al.

10/24 Herry et al. and Courtin et al.

            Herry et al. discovered that different groups of neurons in the BA are involved in fear learning and extinction. They used electrophysiology to isolate these different groups of neurons. When recording these neurons during extinction, they discovered that once extinction neurons began to increase in activity, fear neurons began to decrease one extinction block afterwards. After the neuronal changes, the behavioral changes began to occur; freezing began to decrease. The changes were measured with the change point analysis algorithm. I had never heard of the change point analysis algorithm before, and I thought it was an interesting approach to see if the neuronal changes actually corresponded with the behavioral changes. After fear renewal, extinction neurons were active 7 days after extinction. It would be interesting to test these neurons after a longer period of time to see if the activation of extinction neurons was more chronic. Also, after fear renewal, the extinction resistant neurons were not shown. I was curious why the authors chose not to show the data. Some additional data I would like to see is the raster plot for the rest of the experiments in the data set. It was present in the first figure but not for the rest of the experiments in the paper.

            Courtin et al. used an auditory fear conditioning paradigm that was similar to Herry et al., but used different techniques to look at populations of neurons. They recorded interneurons and principal neurons in the dmPFC that were responsive to CS+. The researches silenced type 2 (PVINs) interneurons using a cre-recombinase AAV. They were shown to have a large role in fear behavior. Optical silencing of PVINs by ChR2 before fear conditioning transiently induced freezing. The researchers also discovered that principal neurons (PNs) disinhibited during CS+ presentations preferentially targeted the BLA. This is because theta phase reseting by PVINs synchronizes PNs after CS+ presentations, and dmPFC PNs preferentially target the BLA to drive fear responses.

Fear Expression / Fear Extinction - Joe

Herry et al vs Courtin et al

Seminar in Biopsych

Fall 2016

Professor Shansky

It is a longstanding finding that cued fear conditioning leads to a marked increase in fear expression in mice, as indicated by a strong increase in freezing behavior (along with coupled autonomic responses, such as increased corticosterone, increased heart rate, etc.). Furthermore, it’s known that continuous presentation of the conditioned stimulus without the unconditioned stimulus is enough to dissociate the association between the CS and the US. Lastly, it’s been shown that reintroducing a contextual cue (the initial context that a mouse was conditioned in) is enough to recover the fear response that was extinguished. This is a good model for PTSD in humans, the most popular treatment for it — exposure therapy —, and its lack of efficacy: most people that undergo exposure therapy experience spontaneous recovery. What circuits mediate this behavior and what about the nature of the circuit leads to this strange series of behavioral changes? In other words, how do fear memories get stored and accessed, and when we try to forget them, why are they not efficiently forgotten?

Herry et al tried to unveil the inner workings of this fear circuit by taking a look at the basolateral amygdala (BLA), a brain structure previously implicated in fear memory consolidation and expression. By using an electrophysiological approach to record from these neurons, neurons with firing patterns correlated specifically with certain stimulus presentations were able to be dissected out. They segregated two groups of neurons that were responsive to the CS+ (that was originally paired with the US) either after fear conditioning or after fear extinction; these neurons will collectively be referred to as the fear ensemble and the extinction ensemble, respectively. Furthermore, they showed that the pattern of activity of either ensemble preceded the respective behavioral changes (freezing increase/decrease). Interestingly, they observed that fear renewal was coupled with a decrease in the activity of the extinction ensemble and a preferential recruitment of the fear ensemble, once again. This is in line with the hypothesis that this collection of “fear neurons” directly control the switch to a fearful state. An interesting question to ask is what the “extinction-resistant” population of neurons represents, and how they contribute to the modulation of this behavior, if at all. The BLA is a very heterogeneous brain region, so maybe it has nothing to do with fear at all. 

Okay, so there are two discrete populations of neurons that are more highly active during fearful and “safety” states, but where do these properties of these neurons come from? Are they anatomically different? Herry et al showed that the hippocampus preferentially innervates the fear neurons in the BLA, and those neurons go on to project to the mPFC, while there is a reciprocal connectivity between the BLA extinction neurons and the mPFC. This kind of makes sense, considering the hippocampus has been implicated in memory acquisition, and the mPFC has been implicated in decision making. Lastly, they showed that inactivation of the BLA does not change the current fear state of the mouse, but removes the ability of a change in the emotional state of the mouse.

When Cyril Herry finished his post-doc, he continued to try to probe this fear circuit to understand what is leading to this differential emotional state after behavioral (or neuronal) manipulations. It’s been shown that the mPFC has many different neuronal subtypes, one of which includes parvalbumin interneurons. PV interneurons are a subtype of interneurons characterized by their expression of parvalbumin; they are fast-spiking in nature, and exhibit perisomatic influence on principal neurons. Using a combination of electrophysiological recordings (multi-unit and LFP) and optogenetics (activation and inactivation), Courtin et al was able to show that PV interneurons are necessary and sufficient for fear expression in mice (I hear necessary and sufficient gets you Nature papers). To gain control of just the parvalbumin neuron population in the mPFC, they used PV-cre mice and injected cre-dependent AAVs for optogenetic manipulation. They showed, in several ways, that manipulating the activity of the PVIN population altered the activity pattern of the principal neurons (PN) in the mPFC to, in turn, affect fear expression. They showed that PVIN inactivation reset the theta rhythm in the mPFC, which has previously (and since) been shown to be important for fear expression), and preferentially activated the PN population, to lead to an increase in fear behavior. Great, so there is a modulation of activity in the mPFC that relates to fear expression, but that doesn’t tell us how it fits into the grand scheme of the fear circuit. So lastly, Courtin et al showed that those mPFC PN neurons that were activated during PVIN inactivation targeted the BLA, as shown by antidromic activation of efferents to the BLA with concurrent recording of the mPFC neurons.

These two papers provided deep and thorough insight into the mechanistic nature of the fear circuit; however, how the fear circuit works in its entirety is yet to be unveiled. Herry et al found two segregated populations of neurons in the BLA that showed dichotomous firing patterns paired with dichotomous behaviors, while Courtin et al showed that PV interneuron activity modulates the communication of the mPFC to the BLA. But, where do the PV interneurons in the mPFC receive their projections from, and which BLA neurons get innervated by mPFC projections? It’s in the works!

Both papers ended with just about the exact same closing paragraph: the translational goal of this work, which is to help understand what goes on in anxiety disorders where fear expression regulation goes awry.

Electrophysiology. is. Everywhere.

Electrophysiology can often be a mystifying area of research for anyone who is unfamiliar. I hope that the discussion of these two papers will be enlightening, as I myself still have gaps in my understanding of the methods. However confusing it may be, electrophysiology is a powerful tool for decoding specific circuitry within the brain. Both papers have used this technique to identify key populations of principle neurons and interneurons that play a role in fear conditioning and extinction. These methods heavily relied on mathematical analysis of ephys data to identify neurons as opposed to staining of cell types like many of the previous papers we have read. To me, this raises a question of which methodology is more reliable.

After reading the first paper by Herry et al (2008), I was left wishing that they had done a few more experiments. They determined that there were two populations of neurons controlling fear and extinction. Although they did a cFos study at one point in the paper to show which neurons were recently active, it did not appear that they did any sort of staining to identify what types of neurons these were. They could have stained for NeuN to identify principal neurons and somatostatin for GABAergic interneurons. These could have provided an added level of characterization of the neurons that they were examining if these markers were co-localized with the cFos. I found the second paper to be more comprehensive in its characterization of subgroups of neurons within the dmPFC-BA-hippocampus circuitry.

Another item that I felt was missing from this first paper was in terms of the behavior in the section "BA inactivation prevents behavioural transitions". They found that "inactivation of the BA before extinction training prevented the acquisition of extinction". I was wondering if there was any way for them to have shown that this effect is specific to fear learning. Perhaps they could have completed another behavioral test/ paradigm that would have shown that the animal's ability to learn at all was still intact after being exposed to muscimol.

10/24 fear papers

This week’s papers further discussed fear expression and expanded on the topic by performing experiments revolving around switching it on and off by neuronal circuits and how some PVIN interneurons shape activity to drive fear expression.

The Courtin et al. 2014 paper in particular left me with a few questions. First of all, they kind of lost me when they started discussing theta phase resetting and oscillations in Figure 4 and Figure 5. Maybe it’s just my lack of knowledge in that particular subject matter, but I feel like they could have introduced it and discussed it better since it appears to be a vital aspect of their experiments. If they just touched upon the basics then that would have made it a lot easier for readers like me to follow.

In that same paper, I was a bit confused on Figure 2d. They stimulated PVINs via ChR2, which would ultimately inhibit freezing since PVINs (such as Type 2 PVINs) are inhibited during freezing. The results show that there was a “significant” decrease in freezing in mice injected with ChR2 over the GFP control mice, which makes sense, but in those ChR2 mice, there was still a 60% freezing rate (in the Post FC graph). Not to mention, when you shine no light (thus you do not see effects of ChR2), it’s still about a 60% freezing rate. When you compare that number to Figure 3c, in which PVINs were stimulated with the inhibiting ArchT which would increase freezing, the numbers aren’t too far off. I would have liked to see, however, another graph in Figure 3c showing Post FC (like in Figure 3d) to have a better comparison.