“We could smell so much better if we’d just work together.” – anonymous ORN

The study of sensory systems provides an opportunity to observe the stunning complexity of the nervous system. Even Darwin himself once remarked that the organization of the eye was so intricate that the odds of it arising from natural selection seemed almost “absurd in the highest possible degree.” Dr. Chih-Ying Su at UCSD focuses her research on the functional organization of olfactory systems in an effort to determine how olfactory receptor neurons (ORNs) are able to process a staggering range of different odors without requiring an infinite number of ORNs to do so.

Dr. Su carries out her research in fruitflies and mosquitoes. In such insects, ORNs are compartmentalized into units called sensilla. Although ORNs are thought to respond to specific odorants independently, the functional significance of their compartmentalization into these stereotyped groups is unknown. Dr. Su investigated this organization by analyzing the relationship between pairs of ORNs within a given sensillum. To do this, she would expose the ORNs to an odorant that would elicit consistent firing of one neuron, and then intersperse bursts of a second odorant to produce firing from the second neuron.

Screen Shot 2016-04-25 at 4.21.47 PM

How do two ORNs behave when they are both activated? As it turns out, it appears that the activation of an ORN has an inhibitory effect on neighboring ORNs. Thus, if neuron A is exposed to an odorant consistently and then a second odorant is introduced, the activation of neuron B reduces the firing of neuron A. To rule out the possibility that the second odorant acts directly on neuron A to suppress its firing, the same assay was carried out following ablation of neuron B. Without communication from its neighbor, neuron A failed to demonstrate inhibited firing. The same results were demonstrated when the roles of the neurons were reversed (i.e. neuron A can also inhibit the sustained firing of neuron B), as well as when the neurons were activated by Channelrhodopsin2 rather than by an odorant stimulus.

This phenomenon is known as lateral inhibition. Interestingly, Dr. Su observed that lateral inhibition between ORNs within a sensillum does not rely on classic synaptic transmission. In fact, it doesn’t require synapses at all. She expressed tetanus toxin (TNT) specifically in ORNs to silence synaptic transmission. Even in these genetically-modified flies, lateral inhibition of a similar degree to control flies was observed. Based on Dr. Su’s characterization of these neurons, it appears that the ORNs communicate via ephaptic transmission, which is a special kind of communication that occurs between adjacent neurons via an extracellular electrical field.

The identification of the functional significance of insect sensilla is particularly intriguing because it provides insight into how odorant mixtures are processed. This is a unique approach to the study of olfaction, as many researchers commonly use only single odorants in their experiments. While that is certainly useful, this two-odorant approach allowed Dr. Su to simulate a more life-like scenario inside the laboratory. By virtue of this, her research could give way to novel methods of insect control in the real world. Perhaps it will soon be possible to utilize odorant mixtures to suppress specific ORNs that would normally drive insects towards a specific target via activation of their neighboring ORNs.

To learn more, check out Dr. Chih-Ying Su’s talk in the CNCB Marilyn Farquhar Seminar Room at 4pm on April 26, 2016.

Caroline Sferrazza is a first year student in the UCSD Neurosciences Graduate Program. She is currently rotating in Dr. Rusty Gage’s lab at the Salk Institute where she uses stem cells to study Bipolar Disorder. When she has a chance to ditch the lab coat, she can generally be found anywhere there’s good music or cute animals. Preferably both.

Olfactory Learning: From Molecules to Behavior

“What are you eating? It smells delicious” you hear someone say from across the room as your coworker walks in with a presumably tasty meal. You breathe in as the odor wafts by you and immediately feel sick to your stomach. That nauseating smell belongs to the same meal you bought last week that made you sick. You try to stay but the smell is so repugnant that you end up leaving. If this has ever happened to you, you have experienced aversive learning in action. Learning is critical to our ability to navigate the world. We can learn because our brains ouwormr plastic, meaning that our brains can change as a result of experience. How is learning mediated by changes to our underlying neural circuits? This is the type of question that neuroscientist Dr. Yun Zhang has been studying at the Center for Brain Science at Harvard University. In order to identify the neural mechanisms of learning Dr. Zhang focuses on studying the roundworm, C. elegans. A major advantage of studying these organisms is that neuroscientist have been able to map the connectivity of all 302 neurons of the C. elegans nervous system. Coupled with a variety of molecular, cellular and genetic tools, C. elegans has allowed neuroscientists to identify molecules, neurons and circuits involved in learning.

In order to study learning Dr. Zhang’s lab has focused on olfactory learning which plays an important role in identifying and locating food sources. Because locating food is essential to the survival of all animal species it is likely that the basic mechanisms of olfactory learning are highly conserved. Dr. Zhang first showed that through olfactory learning C. elegans can learn to avoid pathogenic bacteria (Zhang et al. 2005). C. elegans eat bacteria and in the lab these organisms are typically grown on a plate containing a harmless strain of Escherichia coli OP50 bacteria. However, there are some types of bacteria such as the P. aeruginosa PA14 that are pathogenic and cause infection. C. elegans have different preferences for the bacterial odors of these two strains depending on their experience. C. elegans raised on a plate only contain OP50 bacteria showed no difference in preference for OP50 or PA14 bacterial odors. However, if C. elegans were raised on a plate containing OP50 and PA14 showed increased aversion towards PA14. The same was true for C. elegans that were raised a plate contain only OP50 but exposed to PA14 bacteria for four hours before testing. These worms avoided the PA14 bacterial odors much like you would avoid food that made you sick.

What neural circuit mechanisms allow these C. elegans to learn to avoid pathogenic bacterial odors? One clue came from studying the behavior of mutant C. elegans. The study found that tph-1 mutants, which results in a serotonin deficiency, were unable to learn to avoid pathogenic bacteria. Because C. elegans have a small number of neurons the researchers were able to identify the neurons that were not functioning properly in the mutants. They showed that expressing tph-1 in the ADF neuron in tph-1 mutants rescued aversive olfactory learning. They then showed that the MOD-1 seratonin-gated chloride channel was necessary for olfactory learning. These data suggest that serotonin plays a central role in olfactory aversive learning.

Further work in Dr. Zhang’s lab has continued to dissect the neural circuit mechanisms involved in aversive olfactory learning. They showed that two sensory neurons AWB and AWC are required for C. elegans to display their innate preference for PA14 bacteria. ADF neurons and its corresponding modulatory circuit is necessary for aversive olfactory learning to occur (Ha et al. 2010). Ha et al. were able to map out a circuit corresponding to naïve and learned preferences. Recently, Dr. Zhang’s lab identified a variety of molecular components involved in aversive olfactory learning circuits including Gα-proteins, guanylate cyclases, and cGMP-gated channels. Additionally, they showed that neuropeptides mediate food-odor preferences (Harris et al. 2014). While there is still much more to discover, these experiments have begun to demystify how experience-dependent learning occurs at a molecular level.

To find out more please join Dr. Yun Zhang in the CNCB Marilyn Farquhar Seminar Room at 4 PM on April 19th or check out some of her awesome publications on the topic here.

References 

Ha, H. I., Hendricks, M., Shen, Y., Gabel, C. V., Fang-Yen, C., Qin, Y., … & Zhang, Y. (2010). Functional organization of a neural network for aversive olfactory learning in Caenorhabditis elegans. Neuron, 68(6), 1173-1186.

Harris, G., Shen, Y., Ha, H., Donato, A., Wallis, S., Zhang, X., & Zhang, Y. (2014). Dissecting the signaling mechanisms underlying recognition and preference of food odors. The Journal of Neuroscience, 34(28), 9389-9403.

Zhang, Y., Lu, H., & Bargmann, C. I. (2005). Pathogenic bacteria induce aversive olfactory learning in Caenorhabditis elegans. Nature, 438(7065), 179-184.

Andre DeSouza is a first year student in the UCSD Neuroscience Program. He is currently rotating in Dr. Martyn Gouldings lab where he is working on a project investigating the mechanisms of pain and itch in the spinal cord. 

Living on the Edge: how organisms harness dynamical systems to accomplish more with less

Stability is overrated. A cadaver occupies an energetically stable state. Stable systems cannot adapt to changes in their environment. For a living creature, it is more useful to be finely balanced. Like a coin on its edge, the slightest perturbation unleashes enormous changes its state. So it is with hair-cell bundles.

The hair bundle is a phylogenetically ancient sensory structure. It can be found in the mammalian hearing and vestibular systems, and in the lateral line systems of fish and amphibians. In each case the hair bundles detect mechanical disturbances in an ambient fluid, however, the nature of these disturbances does vary from system to system and from organism to organism. The hair bundles of the ears are tuned to oscillatory displacements, those of the vestibular system detect bodily accelerations, and those in the lateral line are sensitive to pulsatile changes in water pressure. These systems simultaneously boast impressive sensitivity and an extreme dynamic range. It is implausible that a linear system, with outputs directly proportional to inputs, could accommodate the millionfold range of disturbance amplitudes that hair bundles routinely process. Thus, these electromechanical structures must be actively applying non-linear amplification to their displacements. The mechanisms of this amplification, however, are poorly understood.

In order to address this mystery, Hudspeth and colleagues first built a dynamical model capable of replicating the sensory performance and variety of states hair bundles are observed to achieve [1]. While I apologize to the reader for including an equation in a blog post, Hudspeth’s relatively simple model consisted of a system of just two differential equations and two variable parameters:

eq1

in which x is the bundle’s displacement, f is the force owing to adaptation, a is a negative stiffness owing to gating of the transduction channel, τ is the timescale of adaptation, b is a compliance coupling bundle displacement to adaptation, K is the sum of the bundle’s load stiffness and pivot-spring stiffness, Fc is the sum of the constant force intrinsic to the hair bundle and that owing to the load, and F is any time-dependent force applied to the bundle. ηx and ηf are noise terms. Parameters a, b, and τ were held constant, and the state space of the system was explored by varying K and Fc; the load stiffness and constant force, respectively. The resulting state-space of these parameter manipulations is displayed in the figure below.

fig1ABC-1

In the white quiescent regions, the hair bundles demonstrate no movement at all. In the green bistable regions, the structure is mostly quiescent, but will occasionally lurch from one configuration to another. In the orange region the hair bundles spontaneously oscillate between configurations. Furthermore, regular gradients of oscillation amplitude and frequency exist within this regime. This is an elegant mathematical tale, but (A) does it exist it biology? And (B) what does this model have to do with amplification? Both these questions are addressed in Hudspeth and colleagues’  experimental paper on the matter [2].

To probe their in vitro state space, a novel apparatus was devised to mechanically clamp bullfrog hair bundles in a manner analogous to the venerable patch clamp. Mechanical force was applied to the hair bundle via a flexible glass filament. The mechanical load, once set, was maintained continuously by optically measuring the hair bundles’ displacement and adjusting the force applied with a piezoelectric actuator. Thus, the state-space mapped out theoretically could be experimentally tested by adjusting the load stiffness and constant force parameters.

In general, the bullfrog hair bundles behaved in a manner consistent with the model’s predictions. In small- and medium-sized hair cell bundles there exists a contiguous region of state-space in which spontaneous oscillations occur. In addition, the amplitude and frequency tunings of these oscillations follow the predicted gradient. Thus, the dynamical model appears to be a good representation of the biological system.

As shown in the figure above, the theoretical state-space contains a special region along the edge of the oscillatory regime. This narrow space, labeled as the line of Hopf bifurcations, is where the dynamical system is least stable: precariously balanced between periodic and quiescent behaviors. This instability allows small perturbations to effect large state changes, or amplification. Indeed, when hair bundles were mechanically clamped to states near the edge of the oscillatory regime and then stimulated with periodic displacements appropriate to their state-position, the degree of amplitude non-linearity in their resonant response was maximized. Furthermore frequency tuning was most narrow in this border region, perhaps indicative of the fragility of dynamical system.

Dynamical systems are common in nature, doubly so in the complex substrates of cellular biology. Force multiplication is not a concept limited to sensory systems. A minuscule concentration of signaling molecule can trigger macroscale developmental patterning, or the release very few hormones can begin complex cascades. The leveraging of unstable dynamical systems to achieve large outputs from small inputs may one day be regarded as a general principle of biology.

Learn more when Dr. Jim Hudspeth discusses  “Making an effort to listen: mechanical amplification by myosin molecules and ion channels in hair cells of the inner ear” at 4 pm this Tuesday, March 29th in the CNBC Marilyn Farquar Seminar Room.

Sources

  1. Maoiléidigh, D.Ó., Nicola, E.M. and Hudspeth, A.J., 2012. The diverse effects of mechanical loading on active hair bundles. Proceedings of the National Academy of Sciences109(6), pp.1943-1948.
  2. Salvi, J.D., Maoiléidigh, D.Ó., Fabella, B.A., Tobin, M. and Hudspeth, A.J., 2015. Control of a hair bundle’s mechanosensory function by its mechanical load. Proceedings of the National Academy of Sciences, 112(9), pp.E1000-E1009.

 

Burke Rosen is a first year student in the UCSD Neurosciences Graduate Program. He is currently between rotations and is interested in using clever signals analyses to make somewhat educated guesses about unfathomably complex phenomena. 

 

 

 

Targeting Pain at its Source

Anyone who’s accidentally touched a hot stove can understand that pain protects us. It’s a warning signal to remove our bodies from noxious stimuli (don’t keep your hand on a stove) and a powerful reminder to avoid them in the future (order take-out). While many acute forms of pain are clearly giving us helpful information about the safety of our environment, chronic pain can be uselessly debilitating.

Chronic pain affects millions of Americans every year. Currently, opioids are prescribed to treat pain in a short term setting. These drugs work by binding to opioid receptors in the brain to reduce the perception of pain. Opioids also act on the reward centers in the brain, which can lead to dependence and abuse. According to the CDC, opioids were involved in 28,648 deaths in 2014. In addition to supporting addiction treatment, a major priority in curbing opioid addiction should be to find alternative drugs for pain management.

So what are some possible alternatives to opioids? One major area of research is to target the primary sensory neurons that detect painful stimuli in the first place. These neurons, called nociceptors, transduce chemical, thermal, and mechanical pain into an afferent signal that lets you know you’re hurt. One group of ion channels that transduces pain signals is the Transient Receptor Potential (TRP) ion channel family. Two particular channels, TRPA1 and TRPV1, have been studied as potential targets for painkillers. However, antagonists that block their function also impair many normal sensory functions, like thermal sensation and protective, acute pain. Because of these side effects, clinical trials of these antagonists failed.

Luckily, the activity of TRPA1 and TRPV1 is complex, and there are more potential ways to reduce their pain signaling than simply turning them off. Dr. Xinzhong Dong and his lab, who also study itch, targeted the interaction of TRPA1 and TRPV1 to see if they could alter pain signaling in primary sensory neurons (Weng et al., 2015).

TRPA1 and TRPV1 can form a heteromer in sensory neurons, and this interaction is thought to be involved in the nociceptive pathway. Weng et al. identified a transmembrane protein, Tmem100, that modulates the TRPA1-TRPV1 complex in a way that increases the pain-related signaling from TRPA1.

To determine if Tmem100 actually affects the perception of pain, Weng et al. performed a barrage of behavioral assays to if pain responses were different in regular mice compared to mice in which Tmem100 had been genetically deleted in primary sensory neurons. Unlike the failed TRPA1 antagonist clinical trials, the results of theTmem100 knockout showed a decrease in mechanical hyperalgesia (an undesired pain) and preservation of important TRP functions like thermal and mechanical sensitivity.

To understand how Tmem100 affects the TRPA1-TRPV1 complex, Weng et al. electrically recorded from patches of membrane where TRPA1-TRPV1 and Tmem100 were located. They subjected the cells to chemicals that normally activate either TRPA1 or TRPV1, and found that TRPA1 was much more likely to respond to the stimulus when Tmem100 was also present.

Next, after showing that Tmem100 binds to both TRP channels, Weng et al. set out to structurally alter the protein. They found a sequence of the protein on the intracellular domain of Tmem100 that was positively charged and likely to be a binding site for TRPA1 and/or TRPV1. When the putative binding site was changed to an uncharged sequence, binding between the mutant protein, Tmem100-3Q, and TRPA1 was abolished, but binding with TRPV1 was unchanged.

Then, when Weng et al. recorded from membrane patches with the TRPA1-TRPV1 complex and the mutant Tmem100-3Q present, they found a surprising result: Instead of increasing the probability of TRPA1 activity like the wild type Tmem100, Tmem100-3Q actually decreased the probability of TRPA1 responding to noxious chemicals. Moreover, they didn’t even need the full transmembrane protein to achieve this reduction in pain signaling. Weng et al. created a cell permeable peptide (CPP) that mimics the new binding site of the Tmem100-3Q mutant and injected it into wild-type mice. In addition to recapitulating the electrophysiological effects of the Tmem100-3Q mutant, this injectable peptide reduced mechanical hyperalgesia in normal animals, while leaving mechanical and thermal sensitivity intact.

Taken together, these experiments identify Tmem100 as a modulator of TRPV1-dependent TRPA1 activity and introduce a mutant CPP as a viable therapeutic agent for local pain relief.

 

figure1

Don’t miss Dr. Xinzhong Dong discuss “Mechanisms of Itch and Pain” at 4 pm this Tuesday, March 8th in the CNBC Marilyn Farquar Seminar Room.

 

Sources:

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6450a3.htm

Weng, H. J., Patel, K. N., Jeske, N. A., Bierbower, S. M., Zou, W., Tiwari, V., … & Geng, Y. (2015). Tmem100 is a regulator of TRPA1-TRPV1 complex and contributes to persistent pain. Neuron, 85(4), 833-846.

Weyer, A. D., & Stucky, C. L. (2015). Loosening Pain’s Grip by Tightening TRPV1-TRPA1 Interactions. Neuron, 85(4), 661-663.

Rachel Cassidy is a first year student in the UCSD Neurosciences Graduate Program. She is currently rotating in Jeff Isaacson’s lab studying the circuitry of the auditory cortex.

 

From Stimulus to Movement, How You Ate That Pie

At any moment in one’s day, we are confronted with a seemingly endless array of stimuli, or sensory events, and a correspondingly large number of choices. Based on incoming traffic, for instance, you might (wisely) decide to not attempt to cross the street. In this case, the stimuli impinging upon your senses include auditory (eg cars honking, or slightly angry folk cursing you out), visual (eg cars flashing their lights, or slightly angry folks making obscene gestures), somatosensory (eg if you feel a car, you may have made a poor decision), and that of any of your other favorite senses.

We know that, at some point, our brains use this sensory information to inform our decisions to commit, or not commit, an action – such as walking across the street, or staying put; in short, whether to initiate, or suppress, movement. During this almost unconscious process, neurons in primary motor cortex (M1), a region of the brain intimately involved in the learning and execution of movements, demonstrate different activity patterns. Although others have suggested that these different patterns may simply reflect different task or movement parameters, David McCormick and his lab propose that these neurons are interacting in order to control the initiation of movement. Specifically, control over movement initiation or suppression may depend on inhibition, although it is unknown how the brain implements this form of behavioral control.

To get at this mechanism, Zagha et al. (2015) first trained mice until they became veritable experts in a simple task – water-restricted head-fixed mice had their whiskers deflected, termed the ’target,’ and the mice then had to lick for the trial to be considered ‘correct’. In 80% of trials there was also a tone presented before the whiskers were deflected, and in this case the mice had to withhold licking until their whiskers were deflected; at that point, licking would allow them to receive their reward (i.e. water; Fig1A, C). These trials were particularly important, as they discouraged impulsivity, as evidenced by the higher hit rate and lower false alarm rate in expert mice compared to novices (Fig1E, F ). Since expert mice were not impulsive, the authors could look at M1 neurons as the mouse was making a conscious decision to not lick (ie a suppression of movement).

figures2.cdr

Fig 1 – Mice become experts! A) The setup B) With more obvious stimuli (i.e. faster speed), the mouse performs better C) Task structure D) Successful trial, with licking after target onset E, F) Mice do better! G) … Unless you inhibit M1

Curiously, by silencing M1 via injection of muscimol, a GABAA agonist, or via optogenetic activation of PV-containing GABAergic interneurons, Zagha et al. noticed that expert mice would have many more false alarms (Fig1G). This implied that M1 is involved in the suppression of movements.

To figure out what was going on in M1, the authors of the study then used loose patch or multi-electrode arrays to record from neurons in layer 5 of M1. They noticed that some neurons had an increase in spike rate after stimulus onset, but before whisking or licking (Fig2A, C), and another population had a decrease in spike rate for the same period of time (Fig2B, D); simultaneous recordings told them that these two populations of neurons co-occurred (Fig2E). By looking at the change in firing rates across the target stimulus on ‘hit’, or correct, trials, they found two populations of neurons, fitted by two gaussians – one with spike rate enhancement, and another with suppression (Fig2I), further supporting their claim of two distinct populations of neurons in M1.

figures2.cdr

Fig 2 – A,C) Some neurons become more active (i.e. enhanced population) B,D) Some neurons become less active (i.e. suppressed population) E) Both populations co-occur during a task F-I) By fitting to Gaussians, we see two distinct populations of neurons (blue and red Gaussians)

Because these neurons could also be active during other epochs of the task, they might not actually represent the anticipation of movement, or the formation of a decision to (not) move. The authors addressed this by looking at the average neural activity of target-modulated neurons across different conditions and epochs of the task. Their activity was stable across tone presentation, which implied that they aren’t representing a sensory response (Fig3A, F). However, after presentation of the target, one population had a sustained increase in activity, and the other a decrease (Fig3B, G). On ‘miss’ trials, these changes in activity were absent or less pronounced (Fig3C, H), and both of these populations of neurons ramped up, or down, their activity in anticipation of licking, even in off-target responses (i.e. false alarms and spontaneous licking; Fig3D, E, I, J). All of this suggested that these two populations of neurons represent an upcoming motor choice.

figures2.cdr

Fig 3 – A,F) Our two populations of target-modulated neurons aren’t modulated by the tone B,G) …But they are modulated by the target C,H) …And you see less modulation when the mouse was incorrect on that trial D,I) They can also be modulated in anticipation of an action (i.e. licking) E,J)…Even when the movement is incorrect

Since it seemed that these neurons were involved in the anticipation of movement, and their activity was anti-correlated (Fig4F), the authors next investigated the possibility that they inhibited one another, as that is one possible mechanism that could produce anti-correlated activity. Simulations verified that lateral inhibition could produce anti-correlation (Fig4A-D), and the authors, by using Granger causality, found that the enhanced firing (Enh) and suppressed firing (Supp) populations inhibited one another, and that this could be behind their anti-correlated activity (Fig4D, F). Furthermore, the authors simulated their competitive ensemble model (Fig5A), and found that a transient stimulus could be turned into a sustained response due to its intrinsic dynamics (Fig5B). In fact, their simulated data was qualitatively similar to the neural data they observed (Fig5C-F).

figures2.cdr

Fig 4 – A-D) Four different two-ensemble circuit models. You can get anti-correlated activity by either anti-correlated inputs (B), or lateral inhibition (C,D), or both. E) The Enh and Supp populations are anti-correlated F)…And it seems that mutual inhibition is to blame

The authors also found that some neurons were modulated by the target only, by the motor command only, or by both the target and the motor command. This motor-specific population of neurons displayed ramping activity, which could be used to trigger movement if a movement command is issued only when the firing activity of the population reaches a bound (this process is called accumulation-to-bound; Fig6A-F). Since these neurons modulated their activity late in the decision process, they were likely not involved in the aforementioned state transitions (Fig5B).

 

dm5.png

Fig 5 – A) Competitive ensemble model B) Phase plan shows how a transient stimulus can lead to a miss or hit C,D) Two simulations, which depict how both ensembles’ firing activity change when the stimulus does (red or blue) or does not (black) lead to a stable transition E,F) It’s cool that the neural data seems to follow along with the simulations

 

figures2.cdr

Fig 6 – A-C) Some neurons have enhanced activity, where the peak activity is aligned to the target, and likely act as sensory representations. Note how the timing of their peak activity is unrelated to the mouse’s reaction time (RT) D-F) Other neurons show ramping activity, where the peak is aligned to movement, not the target. Here, RT does matter, with earlier peaks for quicker RTs

Therefore, it seems that M1 neurons, with different activities in anticipation of movement, inhibit one another, and can use a transient stimulus to transition from one state of their circuit to another. The authors suggest that these ‘sensory’ neurons can then drive ramping activity in motor-enhanced neurons to trigger a movement, and this could be carried out via the sequential activation of overlapping populations of neurons. The authors carried out further analyses, and found that slow, oscillatory dynamics were correlated with poor performance in the task, seemingly by disrupting anti-correlated activity between the two populations of target-modulated neurons. The mechanisms by which oscillations disrupt anti-correlation, however, remains to be addressed.

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In short, it should be worth it to come to the CNCB Marilyn Farquhar Seminar Room, on Tuesday, 3/1/16, at 4.00 pm, to hear Dr David McCormick give his talk titled “Neural mechanisms of optimal state”

The article which this post was based on is here. Enjoy!

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Javier How is a 1st year student currently rotating in Dr Takaki Komiyama’s lab, where he learns about learning. Due to blistering feedback, he no longer writes haikus about complex topics, as evidenced by this little gem

My name is Javier,

I no longer write haikus

I was told to stop

The workings of working memory (and other cool things the prefrontal cortex does)

It’s midnight on a chilly February evening, and you’re waiting at the station. A train pulls in on another platform, and through the window, you think you see your long lost love from elementary school. Just as the doors are closing, you make eye contact, and an electric spark of recognition passes between you. As the train slowly pulls away, he/she breaths heavily against the window and writes a phone number in the condensation. You have no time to get out your phone. You sprint across the platform, all the while repeating those digits in your head. Now, the train is out of sight, and all you have is your memory. Can you recall them?

If you answered “yes” to the above question, then congratulations, you have confidence in your working memory! Besides getting you a date, the circuits in the dorsolateral prefrontal cortex involved in working memory also underlie abstract thought, and are disrupted in schizophrenia and Alzheimer’s disease. This is the subject of Amy Arnsten’s research.

Consistent with its transient nature, working memory relies on temporary changes in synaptic strength within a network, in contrast to LTP. This process, termed dynamic network connectivity, is accomplished through neuromodulators without altering synaptic architecture.

For a closer look, this post will discuss Wang et al 2013, which assesses the role of NMDA receptor signaling in working memory. The neural basis for working memory is represented by a network of Cue, Delay, and Response cells. During spatial working memory tasks in primates, populations of Delay cells exhibit sustained firing in the absence of sensory input, doing so through recurrent connections. Delay cells receive input from Cue cells, which fire during the onset of visual stimulus, and relay information to Response cells, which project to the motor system. This network contrasts with those of rodents, which have combined Delay and Response cells.

Resus monkeys performed a task in which they were required to recall the location of a visual cue after a 2.5 second delay. In the presence of systemic ketamine, an NMDAR antagonist, single cell recordings of Delay cells and Response cells during this task revealed decreased Delay cell firing (Figure D), but increased Response cell firing (Figure F), concurrent with reduced performance on the task (Figure A). Ketamine has been used as a model for schizophrenia, and weakened NMDA signaling is linked to schizophrenia.

(From Wang et al, 2013, Figure 7)

By using primates instead of rodents, this study refines previous models for schizophrenia. Even though systemic ketamine in humans overall reduces activity in the prefrontal cortex, as measured through MRI BOLD response, systemic ketamine administration in rodents increases firing in the prefrontal cortex. This difference is likely accounted for by the absence of separate Delay and Response cells in rodents. Rodent models of ketamine administration have led to the development of the hyperglutamate theory for schizophrenia, which points to increased glutamate signaling as a mechanism of disease.

These results are directly applicable for developing mechanisms to treat disease (as is representative of Amy Arnsten’s lab, see her work developing guanfacine as a treatment for ADHD http://medicine.yale.edu/lab/arnsten/research/guanfacine.aspx). The hyperglutamate theory resulted in clinical trials using NMDA antagonists as a potential form of treatment for schizophrenia. Consistent with the results of Wang et al, these drugs were either ineffective or worsened symptoms. In light of this research, NMDA agonists may be a more effective treatment.

Amy Arnsten received her PhD from UCSD. Her work represents an exceptionally talented form of advocacy for mental health, as she was originally inspired by the inadequacy of mental health care during her high school volunteer work (see http://www.medicineatyale.org/janfeb2010/people/peoplearticles/55147/ for more information).

Please join Amy Arnsten herself in the CNCB Marilyn Farquhar Seminar Room at 4 PM on February 23 to hear more about her research.

Wang, M., Yang, Y., Wang, C.-J., Gamo, N. J., Jin, L. E., Mazer, J. A., … Arnsten, A. F. T. (2013). NMDA Receptors Subserve Persistent Neuronal Firing During Working Memory In Dorsolateral Prefrontal Cortex. Neuron77(4), 736–749. http://doi.org/10.1016/j.neuron.2012.12.032

Stephanie Bohaczuk is a first year graduate student in the Neurosciences program. She is currently rotating in Jerold Chun’s lab, focusing her research on genomic mosaicism. She also enjoys long walks on the beach (or in the desert, or in the mountains, or just about anywhere).

What can a singing bird teach us about a jump shot?

image

Do you play music or sports? When you first learned how to play, you probably started by copying someone who was more experienced (like a coach, or an instructor), and you probably had to practice quite a bit in order to become good. Have you ever wondered what was going on in your brain when you learned these skills? The answer might actually lie in the brain of a musical bird called the zebra finch.

Zebra finches take courtship very seriously because they are monogamous. Males perform a song and dance to woo females. It is apparent that their song is vital to their mating rituals. Each bird’s song is unique, but it shares similarities to the song of that bird’s father. That’s because zebra finches first learn to sing by copying their dads. They start by “babbling,” then imitate each note that their father sings until they develop their own stereotyped song.

In the last few decades, neuroscientists have started to study how the zebra finches learn their songs. As a result, they’ve discovered some stunning likenesses to our own brains. For example, we now know that the birds learn and produce songs with a series of brain regions which are much like the parts of our brain that plan out motor sequences (such as swinging a tennis racket or playing scales on a musical instrument).

There are quite a few parallels between zebra finch songs and these learned motor sequences. Almost every one of these sequences starts out as an awkward jumble of partial actions. A novice musician has to think about each note in a scale as they’re playing it, while a professional can play almost any scale off the top of their head. An amateur basketball player might not have a very polished jump shot, but with coaching, they can make their motion more fluid. Just like the finches’ songs, a human’s learned motor sequences are very individualized. For example, if you look at many baseball players throwing pitches in slow-motion, you’ll notice tons of small differences in their deliveries.

When neuroscientists study zebra finch songs, they hope to learn more about how we create, develop, and maintain these personalized behavioral sequences. Imagine being able to see everything happening inside Joshua Bell’s brain as he became a violin virtuoso, or looking inside Ken Griffey Jr.’s brain as he develops his swing.

Want to know more about the state of the art in zebra finch research? You’re in luck! Michael Long, a neuroscientist at NYU, is coming to speak at UCSD as part of the DART Neuroscience Seminar Series.

What type of stuff is he going to talk about? In one recent publication, he described a mechanism that the birds use to prevent changes to a song once it’s been learned. It’s kind of like the finch’s version of saving a document as “read only.” Dr. Long found this mechanism by studying a finch brain area called HVC (which is analogous to the human “premotor cortex,” an area we use for action planning). Previous work has shown that HVC neurons become more active when anesthetized birds hear their tutor’s song. The Long Lab has developed a sophisticated way to monitor HVC cells in awake birds. They noticed that, when the finches weren’t unconscious, HVC activity during songs actually changed over development: juvenile animals had lots of activity, while adult birds had very little.

Through a series of very technically challenging experiments, Dr. Long and his lab discovered why this change occurred. They showed that HVC was actually being inhibited when the finches heard part of a song from their tutor that they already knew well. Since the adult birds knew their songs already, their HVC neurons were practically silent. The researchers proposed that this inhibition of HVC was a mechanism for protecting the developing song from being changed.

This is a huge finding in the field. It confirms that the song-learning circuit is specifically “tuning out” parts of songs that it already knows, and (most importantly) proposes a mechanism for how the bird brain can do that. There’s a good chance that our brains use analogous mechanisms to learn motor skills from a tutor or coach; perhaps someday scientists can apply this knowledge to human motor actions.

If you’re interested in learning more about zebra finches, come to The Marilyn Farquhar Auditorium this Tuesday at 4 PM (in the Center for Neural Circuits and Behavior building). It’s going to be a great lecture.

Sam Asinof is a first-year grad student in the UCSD Neurosciences program.  He’s currently rotating in Dr. Tina Gremel’s laboratory, where he studies how mice form habits.  In his free time, he loves to both play music and watch sports.   

Exploring the Dichotomous Consciousness

“One individual studied well, and thoughtfully, might enable you to draw conclusions that apply to the entire human species.”

-David Roberts, Professor of Surgery and Neurology at Dartmouth-Hitchcock Medical Center

The fascinating story of the split-brain patient dates back to the 1940’s. You might rightfully ask: “What is a split-brain patient?”

brain-split

Split-brain patients are individuals who have been plagued by intractable epilepsy — so much so that they were willing to undergo split-brain surgery, which is essentially a procedure that severs the connections between the left and right hemispheres of the brain. This surgical procedure was meant to prevent the spread of seizure activity from its site of origin, thereby controlling the occurrence of debilitating epileptic seizures. The procedure is also known as a corpus callosotomy because the anatomical structure that connects the two hemispheres of our brains is called the corpus callosum, the so-called highway system of information transfer in the brain.

“It was a total shot in the dark.”

– Michael Gazzaniga

The first group that investigated these patients in the 1940’s claimed that there were no significant cognitive or behavioral impairments as a result of split-brain surgery. Fast forward to the 1960’s and along came Michael Gazzaniga, a driven young student at Dartmouth. During his junior year, a Scientific American article on how nerves grow piqued Gazzaniga’s interest, so he wrote a letter to the author, the one and only Roger Sperry, one of the biggest names in neurobiology. In his letter, Gazzaniga inquired about research opportunities — a move he now refers to as a “shot in the dark” — and landed an NSF summer fellowship at Caltech.

gazz2

Gazzaniga as a student at Caltech in 1963

Sperry’s group at Caltech had been studying split-brain rats, cats, and monkeys for some time, and were observing dramatic effects on behavior, which raised a huge question mark in their minds about why earlier assessments of split-brain humans had not revealed significant post-surgical differences. They hypothesized that surgeries done in the 1940’s had not severed the corpus callosum and anterior commissure completely. Gazzaniga was thus tasked with coming up with novel and better ways of testing split-brain patients. So he did…

And his findings introduced the notion of functional lateralization to the field of neuroscience:gazz3

When split-brain patients were presented with visual information (such as an object or a word) in their right visual field, they were able to verbally identify the stimulus. Interestingly, if visual information was presented in their left visual field, patients were unable to do so — in fact they would typically say, “I don’t know.” To understand this phenomenon we must recall the following generalizations:

  1. Information in the right visual field is known to be processed by the left hemisphere, and information in the left visual field is known to be processed by the right hemisphere (see above figure).
  2. Certain aspects of language are known to predominantly reside in the left hemisphere of the brain.

From his observations, Gazzaniga came to the conclusion that split-brain patients were unable to verbally identify stimuli presented in their left visual field because, though the information would travel to the right hemisphere, it would not be transferred to the left hemisphere where ‘language resides’ due to the severed connection between the two hemispheres.

There is a twist however. Patients who stated that they “did not know” what the stimuli presented in their right visual field was were able to draw what they saw with their left hand. These observations along with many many follow-up studies testing for part-whole relations, apparent motion detection, mental rotation, mirror image discrimination, etc. led to the idea that there is perhaps a right hemisphere dominance for visuospatial processing. These ideas are not meant to be mutually exclusive for one hemisphere or the other. In fact, one patient clearly demonstrates that certain aspects of language, such as spelling, can also reside in the right hemisphere: P.S., a teenager split-brain patient, was asked “Who is your favorite girlfriend?” with the word ‘girlfriend’ flashing only in his left visual field. He was unable to answer the question verbally because the information remained in his right hemisphere; however his left hand (controlled by the right hemisphere) was able to select Scrabble letters and align them to spell’L-I-Z.’

gazz4.jpg

Split-brain patients were the key to studying the functions of the two hemispheres independently, and Gazzaniga recognized the value in capitalizing on what this unique patient population had to offer to the advancement of neuroscience. Among his many accomplishments are serving on the President’s Council on Bioethics between 2001-09, basically founding the field of cognitive neuroscience with fellow psychologist/linguist George A. Miller, and being awarded the Guggenheim Fellowship for Natural Sciences. Come hear him talk on lessons learned from split-brain research this Tuesday, January 12 at 4 pm.

Ege A. Yalcinbas is a first-year student in the neurosciences graduate program currently rotating in Dr. Chalasani’s lab. Michael Gazzaniga was one of the first neuroscientists she read about in high school so she is excited to fangirl him at his talk on Tuesday.

References:

Goodnight Fly: What Drosophila Can Teach Us About Sleep and Memory

In a lab studying the fly

There was a beaker

And a latex glove supply

And a microscope sitting

Next to a guy

And there was a help tab

Open in Matlab

And a fly mushroom body

And a desk that was sloppy

And a genetic screen

And a cabinet of caffeine

And a channel and a cell

And a silica gel

And industrious, brainy lab personnel

 

Goodnight fly

Goodnight guy

Goodnight microscope next to the guy

Goodnight tube

And the glove supply

Goodnight Matlab

And goodnight computer tab

Goodnight desk that is sloppy

And goodnight mushroom body

Goodnight genetic screen

And goodnight forms of caffeine

Goodnight science journal

And goodnight rhythm diurnal

Goodnight activation channel

And goodnight APL cell

Goodnight serotonin

Goodnight silica gel

And goodnight to the hardworking lab personnel

Goodnight GABA molecule

Goodnight DPM pair

And goodnight sleeping flies everywhere

– by Margaret Wise Brown (with slight revision by Anja Payne)

 

Like the ever-wise Margaret Wise Brown, Leslie Griffith and her lab are interested in how sleep is generated. Unlike Margaret Wise Brown, however, the Griffith lab’s interest in sleep goes beyond the parental desire to ease bedtime routines. The Griffith lab is working to answer key questions about sleep such as how the brain changes during sleep and whether these changes are involved in learning and memory.

In order to investigate these questions, the Griffith lab uses Drosophila Melanogaster to probe the role that specific neurons play in sleep and memory consolidation. In their recent paper (Griffith et. al, 2015) they demonstrate that the dorsal paired medial (DPM) neurons are able to promote sleep. Since DPM neurons are also involved in memory consolidation, this paper is one of the first to identify a shared anatomical location for both sleep and memory.

The effect of DPM neuronal activation on sleep occurs via the release of serotonin (5HT) and GABA onto α’/β’ mushroom body (MB) neurons. Activation of DPM neurons during memory consolidation results in inhibition of α’/β’ MB neurons. Since α’/β’ MB neurons are wake-promoting, inhibition of these neurons results in a decrease in sleep. The discovery that DPM neurons inhibit MB neurons is a novel finding and raises many questions regarding the circuit-level function of these neurons.

Several explanations for the utility of DPM inhibition have been proposed. One proposed explanation is that inhibition of α’/β’ MB neurons may impose a directionality on MB feedback which would lead to memory transfer and consolidation. This explanation parallels findings in mammals that show that broadly-projecting inhibitory neurons may coordinate excitatory activity in the hippocampus and neocortex which may control the timing of replay events during sleep.

 

For further information, and for the benefit of your own edification, come see Dr. Leslie Griffith perform on Tuesday, January 5th, 2016 in the CNCB Marilyn Farquhar Seminar Room and/or refer to the paper below.

Haynes P. R., Christmann B. L., Griffith L. C. (2015). A single pair of neurons links sleep to memory consolidation inDrosophila melanogaster. eLife 4:e03868. 

 

Anja Payne is a first-year Neuroscience graduate student at UCSD. Her neuro-scientific interests include almost everything under the sun with an ever-so-slight focus on learning and memory. Her non-scientific interests include zoos, beaches, puzzles, and her adorable, scruffy, mutt.

Exploring the neural basis of semantic concept formation

How can you tell a puppy from a kitten? Ask any first-grade class and you’ll get a range of answers. Some might start with visual information, like whether the animal has whiskers, how its ears look, or what kind of tail it has. Others might use auditory information, like the different sounds of barks and meows. The more creative might want to see if the animals smell or even taste different.

Though none of these answers is any more correct than the others, each reflects a different approach to the problem of categorization. In cognitive neuroscience, the ability to categorize objects is thought to require the existence of a distinct “concept” for each category, containing information about the typical properties, or features, that correspond to items in that category. The exact neural mechanisms by which concepts are formed and maintained, however, are not fully understood. According to one set of theories, concept representations are distributed across sensorimotor areas, with each area representing activation of a specific feature. By this mechanism, knowledge of the concept is a direct result of connections between different feature areas. Alternatively, other theories have proposed the existence of integration areas that connect to multiple sensorimotor regions and contain intermediate representations of concept knowledge. Though the set of features used to define a concept can span multiple sensory modalities (eg. vision, touch), the intermediate representation of a concept appears to exist in a theoretical semantic space, independent of any individual modality.

In a recent paper from the lab of Sharon Thompson-Schill, the role of these intermediate areas was examined using functional magnetic resonance imaging (Coutanche & Thompson-Schill). Specifically, this paper tested whether there is evidence to support the anterior temporal lobe (ATL) as a “convergence zone”, or an area where various feature fragments are consolidated to form a coherent representation of the object concept. During scanning, subjects viewed a screen of visual noise, while attempting to detect the presence of a specific fruit or vegetable within the noise. The researchers analyzed brain activity during the time period before the fruit or vegetable was actually on screen, meaning the subject was actively thinking about the concept for the fruit or vegetable, but was not actually viewing it. These memory-driven neural activation patterns were used to determine the type of information represented by the ATL during concept retrieval, and how it interacts with the feature information represented in other cortical areas.

Key findings:

1. Activation in the ATL is specific to the identity of the retrieved concept, i.e. the specific fruit or vegetable that is being recalled. Using MVPA and a roving searchlight (https://en.wikipedia.org/wiki/Functional_magnetic_resonance_imaging#Statistical_analysis), the authors were able to identify a cluster of regions near the location of the left ATL that had above-chance classification accuracy (below).

Screen Shot 2015-12-13 at 8.34.29 PM2. When a concept is retrieved, areas that represent specific features of the object (in this case, color and shape) are specifically activated. Again using MVPA, the authors identified a bilateral region of lateral occipital cortex (LOC) and right V4 as areas that encode feature fragments corresponding to the concept being retrieved.

3. Successful retrieval of the object identity is concurrent with activation of the feature fragments corresponding to that object. Concurrent decoding of color and shape within feature regions (LOC and V4) was found to be specifically predictive of successful object identity decoding in the ATL. This result further supports the importance of the ATL in binding the information stored in distributed feature areas into a coherent representation of an object concept.

These results demonstrate that the ATL is likely to function as a convergence zone, providing a plausible neural substrate for the formation and retrieval of feature-based object concepts. They also open the door to further speculation about the role of top-down processing in identification of objects by features, as well as how features may be differently weighted depending on the type of sensory information available. Current and future work by the Thompson-Schill lab may address such questions as well as contributing further insights into the neural mechanisms underlying conceptualization.

Source:

Coutanche, M.N., & Thompson-Schill, S.L. (2014). Creating concepts from converging features in human cortex. Cerebral Cortex.