The Persistence of Memory

The Persistence of Memory by Salvador Dali, 1931

The Persistence of Memory by Salvador Dali, 1931

In 1942, Jorge Luis Borges wrote about a boy named Ireneo Funes who, following a horseback riding accident, developed a phenomenal memory. He was able to recall entire volumes (in his non-native language) and recite them fluidly. In contrast to others with hypermnesia (see The Mind of the Mneumonist by A.R. Luria), what made Funes’ memory particularly curious was his inability to forget and the specificity of his memories. When he remembered, for example, a cloud in the sky, he not only remembered the specific cloud, but the time he viewed it, the direction of the wind, if he was hungry, what smells were in the air, etcetera. He even created distinct memories each time he saw an object at a particular angle. Sage from the left would be a distinct memory from Sage from the right.

Though fiction, this 70 year old story brings to the fore an important question regarding memory formation: How do we remember some things and forget others? Surely, there is a tremendous amount of informational throughput: We have countless experiences during the course of even a single day. But why is it that we only remember a minority of these experiences?

Deep in the heart of Missouri, Kausik Si and his team are researching just this. To phrase the question from a more biological standpoint: “How does the altered protein composition of a synapse persist for years when the molecules that initiated the process should disappear within days?” (Majumdar et al., 2012; 515)

Proteins are recycled with a certain regularity (mostly through proteolytic processing by the lysosome). Proteins that stick together and oligomerize (think of legos being stacked together) can be immune to such degradation (a child would have a harder time eating a stack of legos than they would an individual piece). This process has been explored in a pathological context – for example: beta-amyloid plaques and Alzheimer’s disease – but what if it has physiological relevance in a biological context?

Based upon previous studies with the sea slug (Aplysia), Si et al. hypothesized that oligomers of the Orb2 protein could provide a substrate for the persistence of memory.

Through careful biochemical experiments, they determined that Orb2 was expressed as a monomer (one lego) and an oligomer (multiple legos). These were often hetero-oligomers comprised of splicing variants of the orb2 transcript (different color legos). In fact, it seems as though the smaller Orb2a variant, which is very sparsely expressed, plays a catalytic role in the oligomerization of Orb2b. Disruption of Orb2a expression had no effect on memory acquisition, however it showed a marked defect in memory retrieval after 48 hours – thus suggesting that Orb2a expression, and subsequent oligomerization of Orb2, plays a causal role in the persistence of memory.

Figure 7

Figure 7

They performed two separate behavioral memory tests to show that this is a generalizable phenomenon. Let’s focus on the first one, because it is first and also ripe for humorous extrapolation.

In the “Male Courtship Suppression” task, males are exposed, repeatedly, to unreceptive females (guys, I think you can relate). Over time, these males, discouraged and probably in the throws of a melanogaster-existential crisis, suppress their haughty courtship and stand down. However, flies with a mutation in the Orb2a isoform had no difficulties remembering being spurned in the first 36 hours (figure 7c). But within 48 hours, they were back on the horse, pursuing the same unreceptive female (ladies, you may know the type).

This suggests that the Orb2a isoform is necessary for the persistence, and not the formation, of long-lasting memories.

While further work needs to be done to explore how these oligomers represent a memory at the level of a single neuron, as well as in a network of neurons, it provides a novel pathway completely distinct to the well-studied activity-dependent immediate early genes. I encourage you all to come see Doctor Kausik Si’s talk at 4PM in the Large Conference Room in the Center for Neural Circuits and Behavior!

Sage Aronson is a first-year Neurosciences student currently rotating in Roberto Malinow’s lab. He spends an inordinate amount of time on a bicycle and has a peculiar fondness for the word “spelunking.”

Borges, Jorge Luis. “Funes the Memorius.” Ficciones. Buenos Aires: Emecé Editores, 1956. N. pag. Print.

http://www4.ncsu.edu/~jjsakon/FunestheMemorious.pdf

Majumdar A., Erica White-Grindley, Huoqing Jiang, Fengzhen Ren, Mohammed “Repon” Khan, Liying Li, Edward Man-Lik Choi, Kasthuri Kannan, Fengli Guo & Jay Unruh & (2012). Critical Role of Amyloid-like Oligomers of Drosophila Orb2 in the Persistence of Memory, Cell, 148 (3) 515-529. DOI: http://dx.doi.org/10.1016/j.cell.2012.01.004 

http://www.sciencedirect.com/science/article/pii/S0092867412000050

Behavioral/physiological tolerance and making regulations based on THC blood levels

crossposting from tlneuro.wordpress.com

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Drug “tolerance” is a fairly simple concept (Wikipedia). It means that with successive exposure to a given drug, in many cases the same dose produces a reduced effect. This can be for any number of mechanistic reasons including a change in the metabolism and/or excretion of that drug, a change in the number or sensitivity of the receptor sites through which the drug interacts with the body or a change in the neuronal circuitry, or physiological processes, that are affected. Of course, things are complicated since tolerance may or may not be produced depending on the behavioral or physiological measure in question, on the drug in question, on the circumstances (dose, freqency, etc) of drug exposure and a whole host of other factors. Drugs can even produce sensitization, which is a progressive increase in the effect with successive exposure.

Legalization of marijuana for medical use purposes in many US states and the recent decriminalization of purely recreational marijuana use in Colorado and Washington states has been associated with an effort to determine legal impairment. This is most typically in the context of the limit for operating an automobile. In WA, the decriminalization initiative set 5 ng THC per mL of blood is the “per se” limit for presumed impairment of the ability to operate an automobile. In Colorado, the State Senate passed a similar limit.

Leaving aside the question of what the limit should be, today I want to discuss a paper that makes some of the issues involved clearer and shows why there are not any straightforward answers.

Ginsburg BC, Hruba L, Zaki A, Javors MA, McMahon LR. Blood levels do not predict behavioral or physiological effects of Δ⁹-tetrahydrocannabinol in rhesus monkeys with different patterns of exposure.Drug Alcohol Depend. 2014 Jun 1;139:1-8.
[PubMed, Journal Site]

Ginsburg and colleagues report the relationship between blood levels of THC and effects on behavior and thermoregulation in rhesus monkeys. The key part of the paper is the comparison between a group of animals who had received twice-daily THC (1 mg/kg, s.c.) or animals who had received lower doses of THC (0.1 mg/kg, i.v.) only every 3 o4 4 days. These are referred to as the Intermittent and Chronic exposure regimens/groups.

Ginsburg14-Fig1The study examined the effect of a 3.2 mg/kg, s.c. dose of THC in each group. The primary outcome measures were rectal temperature (hypothermia is a classical effect of cannabinoids in laboratory models), response rate on stimulus-termination operant procedure and blood levels of THC. Response rate may not be the most complex behavior going but it does tend to be sensitive to general intoxication level. As you can see in Figure 1, reproduced here, the groups differ in the effect of an identical THC dose on both temperature and behavior. The Chronic treatment group had minimal to no response to THC whereas the Intermittent group had a significant drop in body temperature and a slowing of response rate. The key consideration was that there was no difference in the blood levels of THC between the groups. Thus, the tolerance that was observed cannot be due to metabolic tolerance, i.e., a change in the rate of drug metabolism and excretion. Importantly, this means that chronic and occasional users of marijuana being tested for possible DUI will not differ due to metabolism of the drug.

As I noted, this study does not really speak to what blood level would be associated with impaired human driving after THC. The behavioral measure is simply too distantly related for good inference-particularly since driving crashes are more about failures of attention and judgment then about physical control over the car. What it does show, however, is that a given THC blood level is fairly meaningless as a predictor of the impairment of a given individual without any knowledge of that person’s history of exposure to THC.

Hey, pass me a beer


California Dew

 

You’re staring steely-eyed at the camera, when your friend hurls a beer at a wall to glance it towards you. You want to reach and catch that beer, crack it open, and let it spray for the camera. Maybe you’ll get a million YouTube views. Maybe you’ll get sponsored by Old Milwaukee. Maybe this is the day you finally make it in the world. But first you have to reach for that beer.

C’mon, brain, you can do it! But how do you do it? Well, if you close your eyes, you’ll probably miss the beer. No million YouTube views. So we need information from the eyeballs. But you don’t necessarily have to be looking directly at the beer. Any juggler knows that you don’t need to be darting back and forth with your eyes to juggle three balls – you can fixate at a point and space and juggle just fine.  And if you’ve ever caught a ball, you know that you can do that perfectly well without looking at your hands, or even having your hands in your field of view. In so many of the movements we make, there’s a beautiful coordination between our direction of gaze, the position of our arms and hands, and the position of the intended target we hope to catch, push, punch, or slide to unlock.

One way to think about the complexity of this coordination is in terms of references frames. When you say “look left” or “look right,” what you mean is “look left relative to the reference frame of your body.” When you decide to look left, your body is pointing forward, and you want to look left of the body vector that’s pointing straight ahead. But while you’re looking left, you could also just as well say that you’re looking straight ahead, but your body vector is pointing right relative to your gaze vector.

Visual information is initially represented in a gaze-centered reference frame – your retina doesn’t know what the rest of your body is doing, so from its perspective, it’s always pointing dead ahead. But if you’re trying to reach your hand towards a target, your brain must transform and integrate this gaze-centered reference frame into a hand-centered reference frame in order to direct proper reach movement. Say you’re at the Kentucky Derby, and your eyes are tracking California Chrome along the track, your hands clutching your armrests in excitment. But you’re parched, and you want a grab that Mountain Dew in the seatback cup holder in front of you. No matter where you’re looking along the track, the movement your hand needs to make is the same, yet the information about where the Mountain Dew is relative to your center of gaze is constantly changing. If your hand or the Mountain Dew were elsewhere, that’s no problem for the brain either. This sensorimotor transformation of reference frame is exquisitely flexible and exquisitely accurate, and all happens behind the scenes.

What are the identities of the neural substrates that underlie this transformation, and how can their organization inform how this area of the brain plans, computes, transforms, or directs information? Previous studies have led to a consensus that posterior parietal and frontal cortex are important for sensorimotor transformation of reference frames, yet two primary models have emerged with hypotheses as to how subregions within these areas encode and represent relative space. In a hierarchical model, distinct populations of neurons encode individual representations of space centered around separate reference frames. In a contrasting model, encoding of different reference frames does not occur in distinct subregions, but instead single areas encode mixed and even intermediate reference frames.

In a recent paper, Lindsay Bremner and Richard Andersen explore this question by obtaining single-unit recordings in a subregion of posterior parietal cortex in monkeys trained to reach toward a target after a ‘go’ signal. By systematically varying the starting position of the hand, the direction of gaze, and the location of the target, the authors hoped to understand how a reach target is encoded by neurons in posterior parietal cortex area 5d. Do area 5d neurons encode target position relative to hand position, target position relative to gaze direction, or hand position relative to gaze direction? Or do they encode the target location in a combination of these and intermediate reference frames within a single brain region?

Examining the tuning curves of hundreds of neurons during different permutations of hand, gaze, and target locations, and controlling for important potential confounds not previously addressed in other studies, Bremner and Anderson provided strong evidence for a nuanced target encoding scheme in area 5d. In conjunction with previous data from the Anderson lab, their data strongly suggests that distinct reference frames are more strongly encoded in different cortical areas, supporting the hypothesis that there exist modular reference frames encoded by specific brain regions. In area 5d, for example, they discovered that the reach target is most strongly represented in a hand-centered reference frame. However, while this representation is predominant, they found that neurons in area 5d also encode mixed and intermediate reference frames, demonstrating that regional encoding is not entirely exclusive to specific reference frames, at least at the level of specificity examined. Their analytical methodology strongly improves upon what has been performed in previous studies addressing similar questions, and I highly recommend diving into their paper for a more thorough account of their study.

Richard Anderson will be speaking at the Center For Neural Circuits and Behavior large conference room on May 6th, 2014 at 4:00 PM. His talk is entitled “Posterior parietal cortex in action.” Join us there!

Patrick is a first-year student in the UCSD Neurosciences Graduate Program

References

Bremner L. & Andersen R. (2012). Coding of the Reach Vector in Parietal Area 5d, Neuron, 75 (2) 342-351. DOI:

Bacteria: a real pain in the…nociceptor?

If you’re over the age of 10, you’ve probably experienced the joys of having a pimple, and all the pain – physical and emotional – that goes along with it. But have you ever wondered why pimples hurt?

Typically we’ve assumed that the pain of an infection comes primarily from the inflammatory response your body produces to fight off the bacteria – cytokines, prostaglandins, and other mediators that activate nociceptors on pain-sensing neurons in your peripheral nervous system. Much like the misery of being feverish when you have the flu, this inflammatory pain is an unfortunate but necessary side effect of the immune response, protecting your body against invasion. But bacteria can be real jerks all on their own, and Dr. Clifford Woolf’s lab has uncovered evidence that some bacteria can directly activate nociceptors through N-formyl peptides and the pore-forming toxin a-haemolysin (aHL).

Dr. Clifford Woolf, of Harvard University, studies pain, regeneration and neurodegenerative diseases. Dr. Woolf uncovered the phenomenon of “central sensitization”, in which peripheral inflammation and tissue damage leads to sensitization of the nociceptive neurons in the dorsal horns of the spinal cord. This sensitization is mediated by NMDA receptors, and can be treated by opiates. His research on this subject is the driving force behind the current practice of treating pain early (for example, by giving morphine before surgery to preempt post-surgical sensitization). Dr. Woolf’s work has been key to better understanding mechanisms of human pain sensation, and plays an important role in the way that patient pain is treated in hospitals around the world.

In a recent study, his group looked directly at the molecular mechanisms of pain generation during Staphyloccocus aureus infection. Never heard of S. aureus? Maybe you know it as MRSA – that’s right, the antibiotic-resistant form of this bacteria is the bane of many hospitals.

Dr. Woolf’s group injected the hindpaws of mice with S. aureus and, as you might expect if someone injected your foot with a bunch of nasty bacteria, the mice showed mechanical, heat, and cold hypersensitivity within one hour. This lasted for 48-72 hours, with a peak at six hours after infection (Fig. 1a). By examining the kinetics of immune activation, they found that tissue swelling did not correlate with pain (Fig. 1a), and the influx of immune cells and cytokines increased in infected tissue but did not correlate with hyperalgesia (Fig. 1b, c). The bacterial load, however, showed a similar time course as that of pain hypersensitivity, peaking at 6 hours and decreasing over time as myeloid cells ingested the bacteria (Fig. 1d). This pain time course doesn’t quite match up if the inflammatory response is causing the pain – but it does match up with the presence of bacteria at the injury site.

Image

Figure 1: S. aureus infection induces pain hypersensitivity paralleling bacterial load but not immune activation.

The lab decided to examine whether key immune response pathways were necessary for S. aureus-induced pain using TLR2 and MyD88 knock-out mice, which removed the animal’s protection against S. aureus skin infection. The same mechanical and thermal hyperalgesia was seen, indicating that the pain response is not dependent on the immune activation. They also tried removing neutrophils and monocytes, important for immunity against the bacteria by limiting its survival and spread, by injecting a GR1 antibody before infection. This treatment resulted in an increase in mechanical and heat hypersensitivity, accompanied by a higher bacterial load. Finally, using NOD scid gamma (or, if you want to get extra-sciencey, NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ) mice, they saw that knocking out natural killer T and B cells did not alleviate the acute bacterial pain. This seems to indicate that the pain sensation associated with S. aureus injection is not dependent on the immune response.

The strong correlation between pain and bacterial load led Dr. Woolf’s group to examine whether or not bacteria interact directly with nociceptors by applying heat-killed S. aureus to dorsal root ganglia (DRG) sensory neurons. This induced a calcium flux response and action potential firing in a subset of neurons that also respond to capsaicin (the chemical that makes spicy peppers “hurt so good” in your mouth) (Fig. 2a,b).

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Figure 2: N-formylated peptides activate nociceptors.

 

So how do the bacteria activate nociceptors? Woolf and his group targeted N-formylated peptides, bacterial molecules used by leukocytes to mediate immune chemotaxis during infection. Application of fMLF (E. coli-derived) and fMIFL (S. aureus-derived) both induced calcium flux in a subset of DRG neurons that also responded to capsaicin, similar to the response seen with bacterial application (Fig. 2e), and resulted in hyperalgesia when injected into mice (Fig. 2f).

FPR1 is the receptor that recognizes fMLF and fMIFL in immune cells, so the lab tried knocking it out in mice. Fpr1-/- mouse DRG neurons showed decreased calcium flux, and Fpr1-/- mice had reduced mechanical hyperalgesia after treatment with fMIFL relative to wild-type (Fig. 3g).

Image

Figure 3: FRP1 effects on S. aureus pain response.

The lab also targeted aHL, a pore-forming toxin involved in tissue damage and bacterial spread. aHL can assemble pores in cell membranes allowing non-selective cation entry – which might be enough to depolarize cells. Like fMLF and fMIFL, aHL induced calcium flux in nociceptors on DRG neurons (Fig. 4a, b). When injected into mice, aHL induced pain behavior in a dose-dependent manner (Fig 4c). These effects did not involved voltage-gated calcium channels or large-pore cation channels, but did require external calcium – this would seem to indicate that the pores aHL assembles in the membrane are sufficient for depolarization. Knocking out aHL expression in S. aureus led to significantly less hyperalgesia than wild-type bacteria, indicating a robust role for aHL in pain during S. aureus infection.

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Figure 4: aHL activates nociceptors and contributes to injection-induced hyperalgesia.

Dr. Woolf’s group neatly summarized the mechanisms by which bacteria directly activate nociceptors in the diagram below:

Image

N-formyl peptides activate nociceptors by binding to FRP1 and inducing calcium flux, while αHL forms pores in the nociceptive cell membrane allowing cation exchange. These mechanisms both appear to be at play in mechanical nociceptive cells, but other mechanisms, especially those related to heat-sensitive cells, remain to be explored.

Finally, the lab opted to ablate (remove) the nociceptive cells responsible for the S. aureus pain response to examine the role of nociceptors in modulating the immune response. Ablation of these cells led to significantly increased tissue swelling with increased infiltration of neutrophils and monocytes at the infection site and enlarged lymph nodes – indicating that nociceptor ablation led to increased local inflammation. This hints at a role for nociceptors directly modulating immune activation, and bacteria may be directly activating the nociceptors as a means to increase immunosuppression and reduce the ability of the host to clear the pathogen.

So not only do the bacteria directly activation your pain receptors, but they also might be making it harder for your body to fight them off. Makes those bacteria sound extra evil, doesn’t it? Think about that the next time you have a sore pimple – or, if you are blessed with good skin, the next time you have a gnarly hangnail.

 

Be sure to check out Dr. Clifford Woolf’s talk, “Studying human pain in a dish”, at 4 PM on Tuesday, April 29th in the CNCB Large Conference Room, if you’d like to hear more on this subject!

 

Alison Caldwell is a first year student in the UCSD Neurosciences Graduate Program. She is currently rotating under Dr. Chitra Mandyam studying the effects of addiction on neuronal proliferation and morphology in the hippocampus. She can be found on Twitter at @alie_astrocyte


Source:

Chiu I.M., Heesters B.A., Ghasemlou N., Von Hehn C.A., Zhao F., Tran J., Wainger B., Strominger A., Muralidharan S. & Horswill A.R. & (2013). Bacteria activate sensory neurons that modulate pain and inflammation, Nature, 501 (7465) 52-57. DOI:

Developing Gain Control in Single Cortical Neurons

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If you are reading this sentence, it is quite likely that you have heard of “gain control” in a neuroscience context. You may notice that the picture provided above has very little to do with the context in which this blog post shall discuss “gain control”. You may also notice that this blog post has a dry, technical, and boring title, which promises a fair amount of eventually enlightening but difficult-to-wade-through mathematics. Given the limited time and intellect of yours truly, however, there will be no equations in this blog post. Instead, a summary/teaser will be provided.

First, a definition of “gain control” according to Dr. Adrienne Fairhall (University of Washington, Seattle) and others in their 2013 Journal of Neuroscience article1:

“…a neural system’s mapping between inputs and outputs adjusts to dynamically span the varying range of incoming stimuli. In this form of adaptive coding, the nonlinear function relating input to output has the property that the gain with respect to the input scales with the [standard deviation] of the input.”

In other words, it is known that neurons can become more or less sensitive (in terms of the absolute input amplitude required to generate the same response) depending on how variable the stimuli are, i.e. how noisy the inputs happen to be. This gain-control property ensures that neurons extract the relevant information (e.g. determine whether someone in a chattering crowd is calling your name) from constantly varying stimuli in a consistently context-dependent manner (e.g. one has to shout louder in order to be heard when everyone else is, oddly enough, shouting). From both behavioral and neural perspectives, gain control has been investigated in different sensory modalities (visual/auditory) 2, 3, and in different animal models3, 4. It is also known that mature single neurons are capable of gain control, based on electrophysiology3. But where does that capability come from?

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An example of mature neuron with excellent gain control. The legend contains details that may or may not be of interest to you. (Fig. 2, Mease et al.)

Dr. Fairhall and her colleagues chose to investigate this problem with both in vitro recording and biophysical models of single neurons. Recording from developing (E18-P1) and mature (P6-P8) mouse somatosensory cortex neurons revealed that mature neurons had better gain scaling than immature ones. In less ambiguous terms, the “symmetrized divergence” in spike-triggered average stimulus (STA, roughly describing the stimulus variability) distribution, which reflects how different the input–output function shapes (which loosely translates to “gain”) for a pair of STA series are, was smaller for mature neurons than immature neurons. The STA series pairs were generated by applying two stimuli with different standard deviations to the same neuron, as well as by applying the same stimulus to different neurons of the same maturity. Therefore, not only were mature neurons recorded in this study better at intrinsic gain control, they were also better by the same degree over the immature counterparts, suggesting that an intrinsic and consistent developmental programme underlies gain control improvement for this type of neurons.

Top left: histogram showing the propensity of immature neurons to have higher pairwise symmetrized divergence (read: more variable gain scaling). Bottom left: Voltage clamp for immature (P0) and mature (P7) neurons, showing an elevation of sodium currents during development. Right: Each dot represents a neuron with color-coded maturity, again showing an elevation of sodium currents during development. (Fig. 3C-E, Mease et al.)

Top left: histogram showing the propensity of immature neurons to have higher pairwise symmetrized divergence (read: more variable gain scaling). Bottom left: Voltage clamp for immature (P0) and mature (P7) neurons, showing an elevation of sodium currents during development. Right: Each dot represents a neuron with color-coded maturity, again showing an elevation of sodium currents during development. (Fig. 3C-E, Mease et al.)

How might this program work, if it exists? Glad that you asked. The short answer, of course, is “not sure.” But Fairhall and colleagues had a promising clue1:

“We have shown previously that INa increases in density much faster than IK during early postnatal development (Picken-Bahrey and Moody, 2003a).”

Indeed, using a biophysical model of single neurons (EIF, or exponential integrate-and-fire) where a single parameter describes how a fixed spike generating kinetics interact with ion channel expression.  By modifying this parameter (which is inversely proportional to the difference between spiking threshold and effective resting potential), a proportionate change in the ratio between sodium channel and potassium channel numbers was implied, and this parameter alone was sufficient to bring about the improvement of gain scaling observed during in vitro cortical neuron development. In confirmation, using sodium and potassium channel blockers, Fairhall and colleagues found in organotypical culture that partial blockage of potassium channels improved gain scaling (less variability/better distribution coverage), whereas partial blockage of sodium channels did the opposite.

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Two pharmacological manipulations of INa/IK change gain-scaling behavior in agreement with model results. Sample estimate of standard deviation (Fig. 5A-B, Mease et al.)

This series of encouraging results spurred Fairhall and colleagues to further apply the EIF model and test the model neurons for gain control abilities based on their sodium/potassium conductance ratios, as well as for conditions under which the model might fail. For the sake of brevity and clarity, however, this blog post will not go into further details.

Based on the results so far, Fairhall and colleagues proposed that the development of gain control in neurons of mouse somatosensory cortex (and, perhaps, beyond) may be a property intrinsic to the single neuron’s gradual self-mediated differential expression of ion channels. An alternative that remained undiscussed, however, is that the differential rate of ion channel production could be mediated in vivo by, say, astrocytic factors, or even dependent on nascent synaptic activities and subsequent calcium entry. Generally speaking, while manipulating only one parameter in a model with good fit seems to reproduce experimental results, it may also be a good idea to keep in mind that said parameter can be altered in different ways in vivo. Another important factor to consider, of course, is the relatively small sample size used in the experimental part of this study, which would in turn impact the model’s generalizability.

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Mease et al., Fig. 7

As part of the UCSD Neurosciences Graduate Program Seminar Series, at 4:00pm on Tuesday, April 22, 2014, in the CNCB Large Conference Room, Dr. Adrienne Fairhall will give a talk on the computational properties of single neurons, as well as how they interact with network-level functions. Come for what might be a refreshingly basic perspective in this age of “map everything”.

Xi Jiang is a first year student in the UCSD Neurosciences Graduate Program. He is now a rotation student under the guidance of Dr. Mark Tuszynski, studying neural stem cell fate determination.

References:

1. Mease R.A., Famulare M., Gjorgjieva J., Moody W.J. & Fairhall A.L. (2013). Emergence of Adaptive Computation by Single Neurons in the Developing Cortex, Journal of Neuroscience, 33 (30) 12154-12170. DOI:

2. Piëch V, Li W, Reeke GN, Gilbert CD. Network model of top-down influences on local gain and contextual interactions in visual cortex. Proc Natl Acad Sci U S A. 2013, 110(43):E4108-17.

3. Hildebrandt KJ, Benda J, Hennig RM. Multiple arithmetic operations in a single neuron: the recruitment of adaptation processes in the cricket auditory pathway depends on sensory context. J Neurosci. 2011, 31(40):14142-50.

4. Chen Y, Li H, Jin Z, Shou T, Yu H. Feedback of the amygdala globally modulates visual response of primary visual cortex in the cat. Neuroimage. 2014, 84:775-85.

Neuronal memory

Francis Crick‘s astonishing hypothesis (1995) is that “You, your joys and your sorrows, your memories and your ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells and their associated molecules.” Actually, more than a hypothesis, this is the basis of modern neuroscience. Understanding how the tiny cells in our brains can generate everything that is in our mind is what motivates the research of Dr. Ben Strowbridge, a professor of Neuroscience and Physiology/Biophysics at Case Western University. Dr Strowbridge is particularly interested in the mechanisms that neurons use to remember things.

Dr. Strowbridge majored in Biology at MIT in 1984, when he started to develop his passion for neurons and their amazing properties. He received his PhD in Neuroscience in Gordon Shepherd’s laboratory at Yale University, studying local circuits that mediate neural activity in neocortex. Later, he moved to the University of Washington, first as a postdoc with Philip Schwartzkroin, and then as an Assistant Professor, before moving to his currently lab at Case Western in 1998, where he has been investigating the neural circuits in the hippocampus, the brain region that is crucial for the generation of many kinds of memory (he has also been interested in understanding how neurons process and generate the sense of smell, but this is subject for another day).

Dr. Strowbridge has been studying one particular type of memory called short-term memory (or working memory), which is the kind of memory that allow us to remember what we did seconds or minutes ago and, in this way, make sense of the world as a continuous story. Most of these things we will later forget, like what we ate for breakfast this morning, or that phone number that we memorized for a couple of seconds and that disappeared from ours minds seconds later.

As Francis Crick said, this kind of memory also needs to be physically stored somewhere inside our brains, during the time of seconds or minutes that it lasts. The most famous theory, first proposed by Donald Hebb, states that short-term memories can be stored by reverberating activity circulating through networks of neurons that fades after a certain period of time (Hebb 1949). Another possibility is that some neurons with exquisite properties would be able to fire persistently during many seconds after the end of the stimulus and, in principle, could store information during this period of time.

Dr. Strowbridge and a graduate student in his lab, Philip Larimer, decided to look at the circuits in a specific part of the hippocampus called dentate gyrus. Using slices of the rat brain, they started looking for some cellular and/or network mechanism into this brain region that would allow the storage of information for periods of at least a few seconds. Since they were working with brain slices in vitro, Larimer and Strowbridge (2010) had more control of what is going on and could use electrophysiology to record the activity of specific neurons while stimulating axons at precise locations.

Despite looking for reverberating activity at neural networks, which would support Hebb’s theory, what they found was that a specific neuron called semilunar granule cells (SGC) showed plateau potentials and remained firing for seconds after the end of the stimulus. Interestingly, this cell was first described by our godfather Santiago Ramón y Cajal about a century ago and was almost neglected since then.

The semilunar granule cell (SGC) Left: Ramón y Cajal drawing of the guinea pig hippocampal dentate gyrus. A semilunar granule cell, highlighted by the arrow, is located in the inner molecular layer, right above the layer of granule cells (GC), the most common cell type in the dentate gyrus. Figure adapted from Cajal (1995).  Right: Intracellular responses of a SGC to graded stimulation in the perforant pathway (PP - main input to dentate gyrus). Note the plateau potential and the persistent firing that lasts for seconds after stimulation. A GC response is showed below for comparison. Figure modified from Larimer and Strowbridge (2010).

The semilunar granule cell (SGC)
Left: Ramón y Cajal’s drawing of the guinea pig hippocampal dentate gyrus. A semilunar granule cell, highlighted by the arrow, is located in the inner molecular layer, right above the layer of granule cells (GC), the most common cell type in the dentate gyrus. Figure adapted from Cajal (1995). Right: Intracellular responses of a SGC to graded stimulation in the perforant pathway (PP – main input to dentate gyrus). Note the plateau potential and the persistent firing that lasts for seconds after stimulation. A GC response is showed below for comparison. Figure modified from Larimer and Strowbridge (2010).

The firing properties of this cell was then characterized and demonstrated to depend on NMDA receptors and specific voltage gated calcium channels. After characterizing the SGC, Dr. Strowbridge and colleagues also demonstrated that downstream neurons in the hilus of the dentate gyrus receive inputs from SGCs and showed SGC dependent persistent firing. Furthermore, they showed that the activity of these hilar neurons varies, depending on the site of the stimulating electrode, but is reliable at a specific site. In other words, the persistent firing of these cells can discriminate between different stimuli, based on their site of origin, and also on their temporal sequence (Larimar and Strowbridge 2010; Hyde and Strowbridge 2012).

Schematic of hippocampal dentate gyrus showing semilunar granule cells (SGCs) in the inner molecular layer (IML) and their projections to hilar neurons (excitatory mossy cells and inhibitory interneurons). Red indicates active neurons, dashed lines indicate inactive pathways. Open circles indicate inhibitory synapses and closed circles indicate excitatory synapses. Hilar neurons can show different patterns of activity, depending on the stimuli, modulating and refining the pattern of granule cell firing (GC), as illustrated by the scheme.  EC: entorhinal cortex; IML: inner molecular layer; GR/ML: granule cell/molecular layer. Figure slightly modified from Walker et al. 2010.

Schematic of hippocampal dentate gyrus showing semilunar granule cells (SGCs) in the inner molecular layer (IML) and their projections to hilar neurons (excitatory mossy cells and inhibitory interneurons). Red indicates active neurons, dashed lines indicate inactive pathways. Open circles indicate inhibitory synapses and closed circles indicate excitatory synapses. Hilar neurons can show different patterns of activity, depending on the stimuli, modulating and refining the pattern of granule cell firing (GC), as illustrated by the scheme. EC: entorhinal cortex; IML: inner molecular layer; GR/ML: granule cell/molecular layer. Figure slightly modified from Walker et al. 2010.

Therefore, Dr. Strowbridge and colleagues have demonstrated that specific cells in the hippocampal dentate gyrus, the SGCs, and their downstream neurons in the hilus have the potential to store the information related to short-term memory in their persistent firing activity patterns.

In spite of all short-term memory work, use a bit of your long-term memory and don’t forget to join us this Tuesday April 7, 2014 at 4:00PM at CNCB Large Conference Room to hear more about this story from Dr. Ben Strowbridge in his talk entitled “Cellular mechanisms of short-term mnemonic representations in the dentate gyrus in vitro”.

Leonardo M. Cardozo is a first year student in the UCSD Neurosciences Graduate Program. He is currently rotating at Dr. Massimo Scanziani’s lab, investigating if long-range projections can also originate from inhibitory neurons, which would be able to control cortical excitability not only locally, but also at distant sites, coordinating activity across the brain.

Primary reference:

Larimer P. & Strowbridge B.W. (2009). Representing information in cell assemblies: persistent activity mediated by semilunar granule cells, Nature Neuroscience, 13 (2) 213-222. DOI:
Other references:

Cajal S.R.Y. (1995). Histology of the Nervous System of Man and Vertebrates. Oxford University Press.

Crick F.H.C. (1995). The Astonishing Hypothesis: The Scientific Search For The Soul. Touchstone.

Hebb D. (1949). The Organization of Behavior. John Wiley & Sons.

Hyde R.A. & Strowbridge B.W. (2012). Mnemonic representations of transient stimuli and temporal sequences in the rodent hippocampus in vitro.  Nature Neuroscience 15 (10) 1430-1438. DOI: 10.1038/nn.3208

Walker M.C., Pavlov I., Kullmann D.M. (2010). A ‘sustain pedal in the hippocampus? Nature Neuroscience 13 (2) 146-148. DOI: 10.1038/nn0210-146

Sexual Dimorphism Found in Olfactory Processing Circuit

Chances are you have never heard of 14,16- androstadien-3-one (AND), but you have definitely smelled it if ever you’ve caught a whiff of a sweaty jogger who mistakenly forgot his deodorant.  AND is derived from testosterone and is found in male sweat, saliva, and semen. Its exposure has been shown to increase physiological arousal in heterosexual women, but not in heterosexual men1.  In addition to behavioral differences, PET scans revealed increased bloodflow to a specific region of the hypothalamus of heterosexual women and homosexual men in response to AND.  Interestingly, that same region of the hypothalamus received increased bloodflow in response to estra-1,3,5(10),16-tetraen-3-ol (EST) in heterosexual men and homosexual women2.  EST is derived from estrogen and is found in the urine of pregnant women.  These studies suggest a specific hub of pheromone processing for which the stimulating odor is sexual orientation specific. Unfortunately, its hard to delve much deeper than that in human circuit mapping, so lets focus instead on a more tractable species for which we can at least get ideas of how sexually dimorphic odor response might occur.

In the case of the fruit fly, a single odor found on male cuticle, 11-cis-vaccenyl acetate (cVA), can induce differential behavior in males and females.  Males exhibit aggression and decreased courtship behavior while females increase their receptivity to male courtship3.  Vanessa Ruta et al. (2010) set out to explore how the same pheromone, detected by the olfactory receptor type Or67D, could produce differential behavior.  A previous study had found no functional sex differences at the level of olfactory receptor responsiveness nor in the DA1 glomerulus receiving input from Or67D-expressing neurons.  However, they did find axonal branches reaching out from the glomerulus projection neurons to the ventromedial lateral horn in male flies but not in female flies4.  It was unclear if sexual differences extended beyond the axonal arbors of DA1 projection neurons and if a full dimorphic circuit could be found.

Circuit diagrams for identified dimorphic cVA pathways in the male (top) and female (bottom).  From Ruta et al. (2010).

Circuit diagrams for identified dimorphic cVA pathways in the male (top) and female (bottom). From Ruta et al. (2010).

Ruta et al. used sequentially photoactivated GFP and electrophysiology to trace a putative circuit responsive to cVA that was different in males and females.  The difference extended further than the DA1 projection neurons and into the lateral horn where they found clusters of third order neurons in males that was not present in females.  They found that a very cVA responsive male only neuron cluster, DC1, sends axons to a male specific neuropil (the lateral triangle and SMP tract).  At this stage, other sensory input converges on the neuropil and could integrate other behaviorally relevant information.  Finally, neurons from the male specific neuropil project to the ventral nerve cord, where behavioral output is enacted. Thus, Ruta et al. found a fundamentally unique circuit in male flies which may explain differential behavior.  The differences arose after the second stage of olfactory processing and continued at every following stage.  Some next steps will be confirming that this circuit is necessary for the observed male behavior and exploring the female circuit in more detail5.

Please join us in the CNCB Large Conference room on Tuesday, March 18th at 4:00pm to learn more from Dr. Vanessa Ruta on “Defining fixed and flexible neural circuits in Drosophila.”

Dr. Richard Axel will also be visiting the UCSD Neurosciences community this week to present “Order from Disorder: Internal Representations of the Olfactory World” on Wednesday, March 19th at 4:00pm at the Dorris Neuroscience Center Auditorium of The Scripps Research Institute.

Margot Wohl is a first year student in the UCSD Neuroscience Graduate Program.  She also wrote this post about UFOs and Obama? 

Primary article:
Ruta V., Datta S.R., Vasconcelos M.L., Freeland J., Looger L.L. & Axel R. (2010). A dimorphic pheromone circuit in Drosophila from sensory input to descending output, Nature, 468 (7324) 686-690. DOI:

Also:

  1. Bensafi M, Brown WM, Tsutsui T, Mainland JD, Johnson BN, Bremner EA, Young N, Mauss I, Ray B, Gross J, et al. Sex-steroid derived compounds induce sex-specific effects on autonomic nervous system function in humans. Behav Neurosci.2003;117:1125–1134.
  2. Savic I, Berglund H, Gulyas B, Roland P. Smelling of odorous sex hormone-like compounds causes sex-differentiated hypothalamic activations in humans. Neuron.2001;31:661–668.
  3. Kurtovic A, Widmer A, Dickson BJ. A single class of olfactory neurons mediates behavioural responses to a Drosophila sex pheromone. Nature. 2007;446:542–546
  4. Datta SR, Vasconcelos ML, Ruta V, Luo S, Wong A, Demir E, Flores J, Balonze K, Dickson BJ, Axel R. The Drosophila pheromone cVA activates a sexually dimorphic neural circuit. Nature. 2008;452:473–477
  5.  Good review of odor specific sexual dimorphism: Stowers L., Logan D.W. Sexual dimorphism in olfactory signaling. Curr. Opin. Neurobiol. 2010;20:770–775

The thalamus coordinates your attention

Someone that has ever ignored the gorilla in the classic video in which basketball players are passing balls knows how visual attention plays a key role in our perception of the world.

Classic selective attention test video in which a gorilla suddenly appears and walks through basketball players while they are passing balls to each other.  If this video is familiar to you or not, you should watch its new version here: https://www.youtube.com/watch?v=IGQmdoK_ZfY.

Classic selective attention test video in which a gorilla suddenly appears and walks through basketball players while they are passing balls to each other. If this video is familiar to you or not, you should watch its new version here: https://www.youtube.com/watch?v=IGQmdoK_ZfY.

Also using humans and monkeys, but in a slightly different way, Sabine Kastner, a professor at Princeton University, is interested in investigating how attentional selection is generated by the neural networks within our brains.

Dr. Kastner is a great example of someone who successfully switched between academic fields. She started to become interested in neuroscience during her undergraduate years as a philosophy student. The great questions related to brain-mind issues, particularly the basis of consciousness and self-awareness drove her to the amazing world of neurons.  Then, she did her doctorate in Neurobiology at Max Planck Institute, and went to medical school, also in Germany, becoming gradually more interested in the neural mechanism of attention.

Recently, one of the main focuses of Dr. Kastner’s lab has been to investigate how the cortical networks interact with each other in order to process behaviorally relevant sensory information, under attentional selection. Applying electrophysiology and neural imaging techniques in monkeys subjected to a visual flanker task (see schematic figure below), Dr. Kastner’s lab was able to demonstrate that brain regions outside cortex are also involved in attention mechanisms (Saalmann et al. 2012).

Flanker test in Salmaan et al. 2012. The monkey’s attention was drawn to the location of a variable cue, which signaled the location of the target in the subsequent array of six stimuli. To receive a juice reward, while maintaining fixation at the center spot, monkeys had to immediately release the lever after the onset of a barrel-shaped target or after the disappearance of the stimulus array for a bowtie-shaped target.

Flanker test in Saalmann et al. 2012. The monkey’s attention was drawn to the location of a variable cue, which signaled the location of the target in the subsequent array of six stimuli. To receive a juice reward, while maintaining fixation at the center spot, monkeys had to immediately release the lever after the onset of a barrel-shaped target or after the disappearance of the stimulus array for a bowtie-shaped target.

In this work, Dr. Kastner’s research group showed that the pulvinar, a region of the thalamus, which is classically considered a passively relaying of sensory information to the cortex (Saalmann and Kastner 2011), also plays a crucial role in high cognitive functions, specifically coordinating the neural activity associated with attention.Flanker test in Salmaan et al. 2012. The monkey’s attention was drawn to the location of a variable cue, which signaled the location of the target in the subsequent array of six stimuli. To receive a juice reward, while maintaining fixation at the center spot, monkeys had to immediately release the lever after the onset of a barrel-shaped target or after the disappearance of the stimulus array for a bowtie-shaped target.

Models about selective attention predict that behaviorally relevant information is routed through cortical networks and that it depends on the degree of synchrony between these cortical areas. Based on previous findings and due to the pattern of connections of the thalamic pulvinar, which receives the majority of inputs from cortex, rather than sensory areas, and sends output back to cortex, creating cortico-pulvino-cortical loops, Dr. Kastner’s and colleagues hypothesized that this thalamic region would be a good candidate region to control the degree of synchrony between cortical areas.

Recording the activity from pulvinar neurons while the monkeys were performing the flanker task described above, Dr. Kastner’s research group showed that pulvinar neural activity was modulated by attention, comparing the neural activity when monkeys were attending to regions within the receptive fields of the recorded cells versus situations in which the attended spot is outside of the receptive field. This modulation was present during the period in which the monkey maintained spatial attention, from the cue-evoked response until after the array onset.

Mean population activity (± SE) of 51 pulvinar neurons aligned to cue or array (target) onset while monkeys were performing flanker test. It shows that attention modulates neural activity in the pulvinar. Figure adapted from Saalmann et al. 2012.

Mean population activity (± SE) of 51 pulvinar neurons aligned to cue or array (target) onset while monkeys were performing flanker test. It shows that attention modulates neural activity in the pulvinar. Figure adapted from Saalmann et al. 2012.

Then, Dr. Kastner’s research group performed simultaneous electrical recordings from pulvinar and two other high-order visual processing cortical areas, T4 and TEO. They showed that all areas presented higher levels of coherent activity when modulated by attention, especially in the 8 to 15 Hz range (alpha band). Moreover, they applied a statistical test called Granger causality in order to evaluate the influence that one area (e.g., the pulvinar) has on a second area (e.g., TEO), accounting for the influence of other areas (e.g., T4). In this way, they were able to show that the pulvinar causally influenced cortical synchrony even in the absence of visual stimulation (between the cue disappearance and the target onset).

Conditional Granger causality (color-coded) from the pulvinar to V4 (accounting for TEO). It shows that pulvinar drives cortical synchrony in the 8 to 15 Hz range  (alpha band) during the whole period in which the animal is presumably attending to the cue location (Flanker test).  Figure adapted from Saalmann et al. 2012.

Conditional Granger causality (color-coded) from the pulvinar to V4 (accounting for TEO). It shows that pulvinar drives cortical synchrony in the 8 to 15 Hz range (alpha band) during the whole period in which the animal is presumably attending to the cue location (Flanker test). Figure adapted from Saalmann et al. 2012.

Aiming to understand how the neural networks communicate with each other within the brain in order to modulate behavior and our perception of the world, Dr. Kastner hopes to shed light in the great questions about consciousness and self-awareness that originally attracted her attention to the field of neuroscience.

I am sure this awesome work caught your attention. So, please join us this Tuesday March 11, 2014 at 4:00PM at CNCB Large Conference Room to hear more about it from Dr. Sabine Kastner in her talk entitled “Neural network dynamics for attentional selection in the primate brain”.

Leonardo M. Cardozo is a first year student in the UCSD Neurosciences Graduate Program. He is currently rotating at Dr. Nicholas Spitzer’s lab, investigating the mechanism behind the match of neurotransmitters and neurotransmitter receptors during the process of neurotransmitter respecification.

References

Saalmann Y. & Kastner S. (2011). Cognitive and Perceptual Functions of the Visual Thalamus, Neuron, 71 (2) 209-223. DOI:

Saalmann Y.B., Pinsk M.A., Wang L., Li X. & Kastner S. (2012). The Pulvinar Regulates Information Transmission Between Cortical Areas Based on Attention Demands, Science, 337 (6095) 753-756. DOI:

fMRI as an unprocessed movie of the mind

What if neural prosthetics could enhance our sensory experiences, transmit them across thousands of miles to someone else like a text message, or even play back what we dreamed the night before?

That’d be cool.

Neural decoding Neuron issue- notably published after (1)

A whole host of talented neuroscientists, including Dr. Geoff Boynton are interested in developing our ability to reconstruct perceptual experiences from brain imaging.  This problem involves a core issue in neuroscience, neural decoding, or understanding how neurons code for a stimulus, and then going backwards from a neural signature to the stimulus that generated it. For exceedingly simply stimuli decoding can still be a challenge, but Dr. Boynton has made strides in understanding more complex visual representations.

One problem, well known to those who work with fMRI, is the plethora of seemingly contradictory results from labs asking the same questions.  While many scientists are quick to utilize fMRI, relatively little is known about how blood oxygen level dependent (BOLD) signal correlates with underlying neuronal activity. Early on Geoff Boynton was interested in this problem, developing a linear response model that most fMRI analyses build on today. The methodology of fMRI remains an interest for Dr. Boynton (3), as only with a clear idea of what fMRI shows us can we develop robust decoders.

As an example, a few years ago Geoff Boynton and our own Dr. John Serences set out to reconcile why motion perception is almost entirely mediated by the middle temporal (MT) area in primate unit recordings, while novel areas are implicated in motion encoding in human fMRI studies.  They asked subjects to identify the direction of (unbeknownst to them) two categories of random dot patterns, ambiguous (0% coherence) and unambiguous (50 or 100% coherence) as shown below:

RDPs

Indeed, Drs Boynton and Serences found that only in the unambiguous case did all the higher order areas seem to encode for motion, while the MT was the only area still predictably active during the ambiguous trials.

Image

Brain region vs. accuracy in the two coherence conditions

This may mean that other visual areas are encoding other relevant features of the coherent sensory stimulus in the unambiguous case. Since these features are likely eliminated in the ambiguous trials (since the stimulus was incoherent), this study confirms the MT as a specialized “hub” for encoding motion-selectively, and finds this may be due to the convergence of inputs on MT.

So, getting back to reading out my dreams, how can we put together this type of information relating the spatial distribution of voxel activation and perceptual experience? Dr. Boynton’s lab is interested in receptive field models that are able to identify which natural image the subject was looking at from voxel analysis. This schematic from Dr. Jack Gallant’s lab at UC Berkeley is quite useful for understanding how this works:

Image

2008 model for identifying natural scenes (2)

This older model worked well, with 92% accuracy for one subject (shown below) and 72% for another. There is obviously enhanced correlation along the diagonal, and newer models fare even better (4).

Image

Entry aij represents the correlation between measured voxel activity for image i and model predicted voxel activity for image j

I am rather excited to see what comes out of the Boynton lab in coming years. This noninvasive way to obtain an image of the brain’s perceptual state would be hugely influential in all of neuroscience, and would also be the basis for future of neural prosthetics. Can I put my name on some sort of wait list?

Come to CNCB this Tuesday at 4pm to hear Dr Boynton’s talk: “I can get that song out of your head: Decoding perceptual representations with retinotopic and tonotopic maps.”

Stephanie Nelli is a first year UCSD PhD student rotating in the Multimodal Imaging Lab

Cite

(1) Serences, J. T., & Boynton, G. M. (2007). The representation of behavioral choice for motion in human visual cortex. The Journal of Neuroscience, 27(47), 12893-12899.

(2) Kay, K. N., Naselaris, T., Prenger, R. J., & Gallant, J. L. (2008). Identifying natural images from human brain activity. Nature, 452(7185), 352-355.

(3) Boynton, G. M. (2011). Spikes, BOLD, attention, and awareness: a comparison of electrophysiological and fMRI signals in V1. Journal of vision, 11(5), 12.
 

(4) Reconstructing visual experiences from brain activity evoked by natural movies. Shinji Nishimoto, An T. Vu, Thomas Naselaris, Yuval Benjamini, Bin Yu & Jack L. Gallant. Current Biology, published online September 22, 2011.

Making Me Hungry

As you stumble upon this page, deciding whether or not to continue reading, a thought crosses your mind: “I could go for a snack.” What is it that triggers your food-seeking behavior? Many cues can bring on hunger, from the delicious smell of freshly baked cookies, to the mouthwatering description of your favorite dish and the resulting mental imagery. What starts out as a simple thought often leads to action. Are there common brain areas or pathways that are activated to produce the search for food? As the control center for homeostasis in general, the hypothalamus must surely be involved, but is there a particular group of neurons responsible for initiating behavior?

COOKIES!!!

COOKIES!!!

The answer: agouti-related protein, or AGRP-expressing neurons, that are present in the arcuate nucleus (ARC) of the hypothalamus. Dr. Scott Sternson’s group at HHMI’s Janelia Farm Research Campus has shown in previous work that in mice, activating a small subset of AGRP neurons is sufficient to evoke “voracious feeding within minutes.”1 This effect is specifically mediated by projections to the paraventricular hypothalamic nucleus (PVH), a region crucial in coordinating appetite.2 More recently, Dr. Sternson and his team have elucidated this pathway as part of a parallel circuit involving specific subpopulations of AGRP neurons.3

AGRP neuron axon projections; photostimulation of some areas (aBNST, PVH, LHAs, PVT) led to increased feeding, while others (CEA, PAG) did not. (Fig 1A, 1H)3

AGRP neuron axon projections; photostimulation of some areas (aBNST, PVH, LHAs, PVT) led to increased feeding, while others (CEA, PAG) did not. (Fig 1A, 1H)3

In this most recent study, AGRP neurons were selectively targeted for activation using Cre-dependent expression of channelrhodopsin-2 (ChR2). Multiple axonal projection fields were then photostimulated in independent experiments, showing that some areas elicited feeding behavior while others did not, implicating different roles in the projection areas of AGRP neurons.

Axon transduction in individual projection areas using rabies virus favors a one-to-one model of parallel, redundant circuitry. (Fig 3A)3

Axon transduction in individual projection areas using rabies virus favors a one-to-one model of parallel, redundant circuitry. (Fig 3A)3

Interestingly, these AGRP neurons appear to project to specific areas on a one-to-one basis, with little or no collateralization, as evidenced by further experiments using a glycoprotein-deleted (replication incompetent) rabies virus. Injections in various projection areas remained relatively circumscribed, with local labeling as well as somatic labeling, but little to none seen in other areas.

Quantification of AGRP neuron populations based on projection areas; topographic distribution along the anterior-posterior axis is based on targets (Fig 5E, 5F)3

Quantification of AGRP neuron populations based on projection areas; topographic distribution along the anterior-posterior axis is based on targets (Fig 5E, 5F)3

Dr. Sternson and colleagues further characterized the distinct subpopulations of AGRP neurons, noting that out of an approximate total population of 10,000, almost one third projected to PVH. Areas that did not elicit feeding, on the other hand, formed much smaller subpopulations. Another interesting finding was that the subgroups of AGRP neurons were distributed based on the location of their targets, as illustrated in the diagram below:

Summary of findings, showing topographic distribution of individual subpopulations of AGRP neurons based on projection area; blue areas elicited feeding, while those in gray did not; line thickness represents subpopulation size; red outline represents leptin receptor expression (Fig 7U)3

Summary of findings, showing topographic distribution of individual subpopulations of AGRP neurons based on projection area; blue areas elicited feeding, while those in gray did not; line thickness represents subpopulation size; red outline represents leptin receptor expression (Fig 7U)3

Based on these anatomical and functional differences, it was suspected there might be differential regulation of individual AGRP neuron subpopulations as well. Using the immediate early gene product Fos as a marker for activation, Dr. Sternson and colleagues used food deprivation or injection of the hormone ghrelin (which signals energy deficit) to activate AGRP neurons. However, distinct subpopulations appeared to be activated to the same degree regardless of whether or not they elicited feeding. Evidence for differential regulation came with the investigation of the hormone leptin, a negative regulator of AGRP neurons and signal for satiety. It was found that only extrahypothalamic projections expressed the receptor for leptin, making the intrahypothalamic projections insensitive to the hormone’s actions.

Taken together, these findings suggest the existence of a core feeding circuit that can be modulated by several inputs from other areas that are not directly involved in eliciting feeding behavior. Further clarification in terms of the regulation of these circuits may have implications for eating disorders and their treatments. So next time you reach for that cookie, just think for a second about your AGRP neurons. Maybe they caused you to reach in the first place.

Hungry for more? Please join us this Tuesday February 18, 2014 at 4:00PM at the CNCB Large Conference Room to hear from Dr. Scott Sternson about “Circuits and motivational processes for hunger.”

David Adamowicz is a first year student in the UCSD Neurosciences Graduate Program (3rd year MSTP). He is currently working on Parkinson’s disease in the laboratories of Dr. Fred Gage and Dr. Subhojit Roy.

References:

1.        Aponte, Y., Atasoy, D. & Sternson, S. M. AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training. Nat. Neurosci. 14, 351–5 (2011).

2.        Atasoy, D., Betley, J. N., Su, H. H. & Sternson, S. M. Deconstruction of a neural circuit for hunger. Nature 488, 172–7 (2012).

3.        Betley, J. N., Cao, Z. F. H., Ritola, K. D. & Sternson, S. M. Parallel, redundant circuit organization for homeostatic control of feeding behavior. Cell 155, 1337–50 (2013).