Mastering the Art of Neuronal Wiring

If you want a reminder of the “big picture” of neuroscience, look no further than Liqun Luo’s website.  He starts off with a couple fun neuro facts, the first of which is that there are 1011 neurons in the human brain.  In other words, we have almost as many neurons as there are stars in the Milky Way. Doesn’t that make you feel kind of special?  But even crazier than that is the number of synapses. On average, each neuron makes 103 synaptic connections with other neurons, for a grand total of 1014 synapses (or a hundred trillion if you’d prefer).  By the time you go past the power of 1012, the internet has a lot fewer suggestions for how to make that large of a number tangible.  The best I could find is that if you stacked a hundred trillion dollar bills, you could reach the moon and back 14 times. The point is we have a LOT of synapses in our brain. So how is it possible for those hundred billion neurons to properly wire those hundred trillion synapses?

Dr. Luo studies this wiring specificity in fruit flies (with a casual 105 synapses) and mice (with a slightly less casual 108 synapses). We know that proper neural circuit assembly requires a spatially and temporally precise chain of developmental events to form precise connections between specific neurons. But what exactly does that entail? In his lab’s recent paper, entitled “Toll Receptors Instruct Axon and Dendrite Targeting and Participate in Synaptic Partner Matching in a Drosophila Olfactory Circuit,” the steps of neural circuit wiring explained so clearly, it could be part of Julia Child’s cookbook…

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While the axon guidance part has been well studied, how pre- and post- synaptic binding partners identify each other remains poorly understood.  This led Dr. Luo’s lab to conduct a confocal-based RNAi screen of 278 genes through 768 lines to look for wiring specificity molecules in the Drosophila olfactory system.  With every knockdown, the lab would use look for resulting developmental targeting defects of Olfactory Receptor Neurons (ORN) and Projection Neurons (PN).  Interestingly, they found that after a Toll-6 knockdown, there was a dorsal shift of the VA1d PN dendrites (see Figure 1), which normally arborize at the anterior surface of the antennal lobe, and the VA1d ORN axons, which normally project to the VA1d glomerulus. They also found that the knockdown of Toll-7, another member of the Toll receptor family, led to the medial mistargeting of Va1dPN dendrites and ORN axons (see Figure 1). To confirm the findings of the knockdowns, the targeting of ORN axons was studied in both Toll-6 and Toll-7 null mice and dorsal and medial mistargeting, respectively, were again found (see Figure 1).

Figure 1

Figure 1. Identification of Toll-6 and Toll-7 as Wiring Specificity Molecules in an RNAi Screen. All images are single confocal sections of adult antennal lobes, with magenta showing neuropil staining and other colors showing axons of specific ORN classes and dendrites of specific PN classes as indicated. N is number of antennal lobes tested.

Dr. Luo’s lab conducted an extremely thorough characterization of the role Toll-6 and Toll-7 play in axon targeting, so I’ll just touch on a couple interesting experiments that they did.  The Luo lab wanted to know if Toll-7 acts autonomously on VA1d and DA ORNs.  So to see if the production of Toll-7 in PNs was necessary, they performed an RNAi knockdown using the PN-specific promoter Mz19-GAL4. The Toll-7 knockdown in PNs had no effect on the axonal targeting of the ORNs or the dendritic targeting of the PNs.  However, an ORN-specific knockdown of Toll-7, through the Pebbled-Gal4 promotor, led to VA1d axon mistargeting identical to the pan-neuronal Toll-7 knockdown (see Figure 2).  When the antennal lobes of the ORN-specific Toll-7 knockdown flies were stained with an anti-Toll-7 antibody, Toll-7 staining was no longer seen in the anterolateral glomeruli (see Figure 2).  These findings suggest that the ORNs are responsible for the production of the Toll-7 wiring specificity molecule. Interestingly, some targeting defects in PN dendrites were seen with the ORN-specific Toll-7 knockdown.  As PN dendrites are pre-patterned in the antennal lobe before the arrival of ORN axons, it wasn’t thought their arrival affected dendritic target selection. However, this suggests flexibility in PN dendritic target selection based on ORN axon targeting.  Finally, the Luo lab took advantage Mosaic analysis with a repressible cell marker (MARCAM), a technique they had previously developed, to see if Toll-7 acts autonomously on ORNs.  With MARCAM, they created and tagged a sub-population of ORNs that were Toll-7 -/- while leaving the remaining ORNs Toll-7+/+.  Then, they studied single wild-type ORN axons through a population of Toll-7 null ORNs.  Interestingly, there were no targeting defects in the wild-type ORNs growing in the presence of the mutants—indicating that Toll-7 acts autonomously.

Figure 2

Figure 2. Toll-7 Is Expressed in ORN Axons Targeting Anterolateral Glomeruli and Is Required in ORNs

If hearing about any of these crafty experiments or pioneering genetic tools has left you wanting to know more, come to Dr. Linqun Luo’s talk June 9th at 4pm in CNCB. It might be the last seminar of the series, but certainly not the least!

References:

1. Ward A, Hong W, Favaloro V, Luo L (2015). Toll receptors instruct axon and dendrite targeting and participate in synaptic partner matching in a Drosophila olfactory circuit. Neuron 85(5):1013-28.

2. Innumerable Julia Child Photos


Written by Kelsey Ladt, a (currently) sleep deprived 1st year Neuroscience student in Dr. Subhojit Roy’s lab.

Lights, Camera, Action!

Take a moment to think about all of the actions you perform on a typical day. How does your brain allow you to learn, select, and execute these actions? The basal ganglia is a group of subcortical nuclei involved in motor functions, and its dysfunction is associated with neurological and psychiatric diseases including Parkinson’s and Huntington’s diseases. Dr. Xin Jin studies movement, focusing on the basal ganglia, with the goal of answering questions like these about action learning and selection.

Imagine you’re calling someone for the first time. Except it’s 15 years ago and you’re using a landline. Maybe you even had to look up the number in a book. You press each number, one at a time, careful not to make a mistake. The next time you call you’re a bit faster. Eventually what was once a meaningless string of numbers becomes an effortless sequence. You’ve likely created three chunks: the area code, the next three numbers, and the last four numbers. Whether you’re dialing a phone number, filling in credit card information online, or entering your pin at an ATM, you think about the sequence rather than the individual numbers. Chunking organizes memories and actions into a single unit, making them easier to recall or perform. There is evidence to suggest the basal ganglia plays a role in chunking, but how is this represented at the neural level? In a recent paper Jin et al. investigated action sequences in mice, finding neural activity relating to the entire sequence, rather than its elements, in the basal ganglia.

Instead of learning a new phone number, mice leaned sequences of lever presses, motivated by a sugar reward. As you might expect, the mice performed the sequences faster and with less variability with training. While mice learned these sequences, neural activity was recoded through electrode arrays implanted in the dorsal striatum, substantia nigra pars reticulata (SNr) and external globus pallidus (GPe). Some of the medium spiny neurons (MSNs) in the dorsal striatum displayed activity that related to the beginning or end of the sequence, or start/stop activity, as had been observed in previous studies. However, many MSNs displayed an increase or decrease in activity throughout the sequence. Further, this sustained increase in activity correlated with the lever press frequency, consistent with the concatenation process of motor chunking. Importantly, these results show that action sequences are represented as a single action unit at the neural level in the dorsal striatum.

(a) MSN displaying activity related to the beginning and (b) end of the action sequence. (c) MSN showing inhibited and (d) sustained activity throughout the sequence.

(a) MSN displaying activity related to the beginning and (b) end of the action sequence. (c) MSN showing inhibited and (d) sustained activity throughout the sequence.

The striatum projects to the SNr and GPe, forming the direct and indirect pathways. The classical view of the direct and indirect pathways is that of the gas and the break, to facilitate and inhibit movement, respectively. Recordings from these target regions revealed different sequence-related activity, suggesting a distinction in their roles in sequence learning and performance.

More SNr neurons exhibited start/stop and inhibited activity compared to GPe, and more GPe neurons displayed sustained activity with training.

More SNr neurons exhibited start/stop (d) and inhibited (e) activity compared to GPe, and more GPe neurons displayed sustained (f) activity with training.

Striatonigral and striatopallidal MSNs preferentially express D1 and D2 dopamine receptors, respectively. The authors wondered if the difference in sequence-related activity in the SNr and GPe could be seen in the D1- and D2-MSN subtypes using photostimulation-assisted cell identification. By injecting AAV viruses expressing channelrhodopsin-2 (ChR2) in a cre-dependent manner into the striatum of mice expressing cre recombinase in either D1- or D2-MSNs, ChR2 was expressed specifically in these neurons.

(a) A D1 Cre mouse with viral driven expression of ChR2-YFP; note axons targeting SNr (b) A D2 Cre mouse with viral driven expression of ChR2-YFP; note axons targeting GPe.

(a) A D1 Cre mouse with viral driven expression of ChR2-YFP; note axons targeting SNr (b) A D2 Cre mouse with viral driven expression of ChR2-YFP; note axons targeting GPe.

These mice learned the task and neural activity was again recoded. Neurons could be identified as direct or indirect pathway MSNs by their activation in response to light stimulation. More D2-MSNs exhibited inhibited activity compared to D1-MSNs, and more D1-MSNs displayed sustained activity. This mirrors the separation of inhibited and sustained activity observed in the SNr and GPe, and is consistent with their inhibitory inputs from D1- and D2-MSNs.

(k) More D2-MSNs exhibited inhibited activity compared to D1-MSNs, and more D1-MSNs displayed sustained activity. (l) More SNr neurons exhibited start/stop and inhibited activity compared to GPe, and more GPe neurons displayed sustained activity.

(k) More D2-MSNs exhibited inhibited activity compared to D1-MSNs, and more D1-MSNs displayed sustained activity. (l) More SNr neurons exhibited start/stop and inhibited activity compared to GPe, and more GPe neurons displayed sustained activity.

These results confirm the difference in activity during the execution of motor sequences in the direct and indirect pathways and suggest a need for activation of both for action selection. Further, these results underscore the importance of understanding the functional organization of the basal ganglia during action learning and execution.

Interested in learning more? Dr. Xin Jin will be giving a talk titled “Dissecting the corticostriatal subcircuits for action” as part of the Neurosciences Graduate Program Dart NeuroScience Seminar Series on June 2 at 4:00 pm in the CNCB Marilyn Farquhar Seminar Room

Barbara Spencer is a first-year Neurosciences student currently rotating with Dr. Jim Brewer.

Keeping it clean: mechanisms of microglial synaptic refinement

When it comes to the brain, neurons are the superstars. They pass the electrical signals that control our bodies and mind, so they’re pretty important. But have you thought about what neurons would be like without their entourage? Image Kanye without his stylists, makeup artists, assistants, caterers, producers, and drivers – he’d have to worry about doing a lot more work on his own, taking the focus away from his music and his persona. Usually, we don’t think about the people behind Kanye, supporting him and keeping him on track. The same is true when it comes to neurons – they get all the love, and their helpers get left in the shadows.

Your brain is more than its neurons. It also contains glia – the word literally means “glue”. Glial cells make up 90% of your brain matter, and the term covers pretty much anything that isn’t a neuron. Many neuroscientists don’t pay them much attention, but in the last few decades, we’ve begun to learn some exciting new things about the roles played by these glial cells in the brain. For example, astrocytes were once thought to be just “support cells” holding the neurons in place and providing them with nutrients and oxygen, but we now know that astrocytes are essential for helping neurons develop appropriate synaptic connections (1), and many synapses in the brain are enveloped by an astrocytic process that influences neurotransmission and plasticity (2).

This week’s Neurosciences Seminar speaker, Dr. Beth Stevens of Harvard University, studies the ways these under-appreciated cells interact with the immune system, and how those interactions lead to refinement of neural circuits by pruning back unnecessary synapses. The brain has traditionally been considered to be an “immune privileged” site, meaning that foreign antigens could be introduced without inducing inflammation. Because of the limited regenerative ability of the central nervous system, it makes sense that the body would want to reduce inflammatory responses that could damage the brain. That view is changing as we begin to better understand the signalling pathways of the brain’s immune cells, called microglia.

It turns out that microglia are important for synaptic pruning, because that process requires signalling using immune system molecules, part of what is called the “classical complement cascade”. In the body, a protein called C1q will be tagged to the surface of cellular debris. C1q then kicks off a signalling cascade that opsonizes C3 and ultimately results in phagocytosis (or engulfment) by a macrophage, allowing your body to efficiently clear away dead cells. In the brain, these same proteins are expressed at synapses that have been “tagged” for elimination, allowing their identification and phagocytosis by microglia. Dr. Stevens studies these molecules using retinal ganglion cells (RGCs) from the eye, allowing her to look at how changes in synapse elimination affect changes in visual perception. In normal eyes, each retina sends projections to the dorsal lateral geniculate nucleus, and each retina makes connections in distinct, segregate areas within the region – so within a patch of dLGN, you’d only see synapses from the left eye or the right eye, not both. In mice lacking C1q and C3 due to a genetic knock out, Dr. Stevens found that the visual system doesn’t develop properly into its normal, eye-specific territories, and you see a lot of overlap – meaning that without C1q or C3, the RGCs aren’t properly pruned (3). It turns out that expression of C1q is also tied to astrocytes – the presence of astrocytes in the cell culture led to increased levels of C1q in the neurons, implicating some kind of astrocyte-secreted protein factor in C1q expression. Given that astrocytes are known to produce cytokines, and cytokines are known to influence expression of complement genes, this connection is not unexpected – but until recently, the identity of the astrocyte cytokine factor was unknown.

In recent work out of her lab, Dr. Stevens and her team were able to identify this factor through in vitro studies with astrocytes and RGCs: transforming growth factor-β (TGF-β) (4). TGF-β is a cytokine secreted by astrocytes, and when media from those astrocytes was placed on RGCs in culture, C1q increased in expression within 15 minutes (Fig1). The lab decided to focus on cytokine factors, given the connection with the complement pathway, and looked specifically at a few proteins found in the media (Fig2A). Among the candidate protein factors screened, only TGF-β proved to be necessary for upregulation of C1q expression (Fig 2B), as immunodepletion to remove TGF-β from media prevented C1q upregulation. TGF-β selectively upregulated C1q in RGCs and not microglia or astrocytes (Fig 2G), and further investigation implicated a particular isoform of TGF-β as the culprit – TGF-β3 (Fig2D,F).

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Figure 1 – C1q is rapidly upregulated in neurons in response to astrocyte-secreted factors

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Figure 2 – TGF-β is necessary and sufficient for neuronal C1q upregulation in vitro

Furthermore, Dr. Stevens and her team were able to determine that TGF-β regulation of C1q is linked to the period of synaptic refinement in the retina in vivo, showing its importance in living animals, by looking at TGF-β expression at several time points (Fig3). At five dates after birth (P5), the time at which C1q expression in the mouse retina peaks, they saw a similar peak in TGF-β expression selectively in the RGCs, with a drop off by P10 (Fig3A-C). To make sure this TGF-β expression was directly regulated C1q expression, they knocked out TGF-β in the retina (using a Cre driver to prevent its expression in retinal neurons) and using anti-TGF-β antibody to block TGF-β action in normal, wild-type mouse retinas. The TGF-β-Cre knock out (KO) mice showed a decrease in C1q expression in RGCs at P5 and not in other cell types (Fig4), and injection of the anti-TGF-β antibody led to a 40-50% reduction of C1q staining in RGCs  (not shown). This supports the hypothesis that TGF-β directly regulates C1q expression in RGCs.

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Figure 3 – TGF-β expression corresponds to synaptic refinement period in the retinogeniculate system

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Figure 4 – TGF-β signaling is required for neuronal C1q expression in vivo

C1q and C3 expression is seen at synapses of the dorsal lateral geniculate nucleus (dLGN), the thalamic brain structure downstream of the retina, but the source of C1q was not clear until now. Dr. Steven’s lab used immunohistochemical staining to determine that C1q is expressed in the axons within the optic nerve, and that the TGF-β knock-out mice showed significantly reduced levels of C1q in these axons (Fig 4E,F). Looking at C1q expression directly in the dLGN, they found that the knock-out mice had reduced expression levels not seen in the primary visual cortex (V1) (Fig5A-C). Since the knock-out selectively removed TGF-β from RGCs only, this implicates RGCs as a source of C1q for the dLGN. The expression of C1q in these knock-out animals wasn’t just reduced throughout the dLGN; examining more closely, the lab found a significant decrease in C1q expression localized to synaptic puncta (5D-G), where C1q needs to be expressed to signal phagocytosis. This led the researchers to conclude that the majority of synaptic C1q in the dLGN is supplied by RGC neurons synapsing in the dLGN, and that other local cells can’t compensate for the loss of TGF-β in the knock-out animals.

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Figure 5 – Retinal TGF-β signaling is required for complement localization in the dLGN

In the final part of their study, the group examined whether or not TGF-β regulation of C1q expression was necessary for the dLGN to form normal, eye-specific territories. Turns out – it is! By injecting tracer dyes into the retinas of knock-out and wild-type mice, the researchers were able to visualize the territories where the RGCs synapses in the dLGN using two different dyes (one in each eye). In a normal eye, because each retina forms distinct, individual territories, one would not expect to see a high degree of overlap between the dyes in the dLGN. More overlap of the dyes would indicate a decrease in eye-specific territory formation. Dr. Stevens had seen this earlier when she selectively knock-out C1q expression, and a very similar phenotype was seen with the TGF-β retina-specific knock out – segregation of eye-specific territories is reduced in both lines (Fig6A,B). This is perhaps a little easier to visualize in Figure 7, where green and red represent the projections from either eye, and yellow represents the overlap of projections from both eyes. The only change Dr. Stevens saw was an increase in overlap; there was no change in dLGN total area (Fig7C). To demonstrate that TGF-β and C1q are part of the same pathway, the lab injected anti-TGF-β antibodies into the retinas of C1q knock out mice. If these two factors acted by different pathways, one might expect to see a “stacking” of effects, and thus a stronger phenotype – but if they act by the same pathway, the phenotype should be the same. Indeed, this is what the lab saw (Fig7A,B). All of this demonstrates that retinal TGF-β expression is necessary for eye-specific segregation of retinal projections, and that C1q and TGF-β likely act via the same pathway.

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Figure 6 – TGF-β signaling and C1q are required for eye specific segregation and microglia-mediated pruning in the retinogeniculate system

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Figure 7 – Mice deficient in C1q or retinal TGF-β signaling show increased overlap of contralateral and ipsilateral areas in the dLGN

Finally, to connect all of this work to the microglial phagocytosis I mentioned at the beginning of the post, Dr. Stevens used an assay she had previously developed to demonstrate that TGF-β and C1q knock out mice showed reductions in microglial engulfment of RGC inputs to the dLGN at P5 (Fig6D) but no differences in microglial number or distribution (not shown). As the researchers put it, “Taken together, our findings support a model in which retinal TGF-β signaling controls C1q expression and local release in the dLGN to regulate microglia-mediated, complement-dependent synaptic refinement”.

Going forward, Dr. Stevens lab has several questions to investigate. Recent work has implicated a role for neuronal activity in microglial phagocytosis of synapses as microglia appear to target less-active synapses for elimination (5); however, it’s not known whether or not neuronal activity regulates this complement signaling pathway or how it might do so. How does C1q from the RGCs arrive in the dLGN and localize to dLGN synapses? Does TGF-β/C1q signalling play a role in developmental synapse elimination across the CNS, or is this pathway specific to the visual system? And finally, TGF-β has been implicated in the amyloid-beta plaques seen in Alzheimer’s disease (6), and having a deficiency in C1q has been shown to be neuroprotective (7). Is it possible that this complement pathway plays a role in the synapse loss and/or dysfunction seen in Alzheimer’s disease?

Dr. Beth Stevens will be speaking tomorrow afternoon at 4 PM in the CNCB Marilyn Farquhar Seminar Room here at UCSD. If you’re interested in how she’s moving forward to try and answer some of these questions, please join us for what is sure to be a great presentation on the importance of these little immune cells and the full entourage of mechanisms that keep your Kanye-brain healthy, clean, and ready to rock.


Alie Caldwell is a second-year student in the UCSD Neurosciences Graduate Program. She works under Dr. Nicola Allen studying the roles of astrocyte-secreted factors in synapse formation using mouse models of neurodevelopmental disorders. She creates educational neuroscience YouTube videos on her channel NeuroTransmissions and can be found on Twitter at @alie_astrocyte

Citations:

  1. Chung, W.S. et. al. (2015). “Astrocytes Control Synapse Formation, Function, and Elimination”. Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a020370
  2. Araque, A. et. al. (1999). “Tripartate synapses: glia, the unacknowledged partner”. Trends Neurosci. 1999 May;22(5):208-15. doi: 10.1016/S0166-2236(98)01349-6
  3. Stevens, B. et. al. (2007). “The classical complement cascade mediates CNS synapse elimination”. Cell. 2007 Dec 14;131(6):1164-78. doi: 10.1016/j.cell.2007.10.036
  4. Bialas, A.R. & Stevens, B. (2013). “TGF-β Signaling Regulates Neuronal C1q Expression and Developmental Synaptic Refinement”. Nat Neurosci. 2013 Dec; 16(12): 1773–1782. doi: 10.1038/nn.3560
  5. Wyss-Coray T. et. al. (1997). “Amyloidgenic role of cytokine TGF-beta1 in transgenic mice and in Alzheimer’s disease.” Nature. 1997 Oct 9;389(6651):603-6. doi: 10.1038/39321
  6. Fonseca, M.I. et. al. (2004). “Absence of C1q leads to less neuropathology in transgenic mouse models of Alzheimer’s disease”. J Neurosci. 2004 Jul 21;24(29):6457-65. doi: 10.1523/JNEUROSCI.0901-04.2004

If Only Viruses Weren’t so Virulent

Viruses are microscopic (non-living) infectious agents with a some-what terrifying potential to invade our nervous system. In fact, there are likely thousands of latent viral particles lying dormant in your body as you read this blog post, but don’t worry! Fortuitously, viruses such as John Cunningham virus, which infects 70 -90% of the human population, only becomes pathological when it is reactivated during immunodeficiency of immunosuppression. At some point or another, we have all likely been infected with a virus via inhalation, ingestion, an insect bite, or by other means. Evolutionarily, our bodies have developed many protective methods to keep these foreign invaders out of our delicate nervous system. Primarily, the blood brain and blood cerebrospinal fluid barriers, prevent large molecules from invading the brain. Unfortunately, viruses have developed several ways to circumvent these CNS barriers. Viruses can infect vascular endothelial cells and directly cross the blood brain barrier. They also enter the brain through the choroid plexus or circumventricular organs, which lack blood brain barriers. Viruses, such as polio and rabies virus, even migrate from infected peripheral nerves or infect exposed olfactory dendrites to enter the brain. Sadly, infants are the most susceptible to viral infections of the nervous system due to greater proliferation of viral particles as cells differentiate and migrate across the parenchyma during development.

So what do viruses do? CNS viruses can infect neurons in the spinal cord (myelitis), meninges (meningitis), parenchyma (encephalitis) or both (meningoencephalitis). Once inside, viruses hijack the molecular machinery of infected cells, replicating and assembling new viral particles. This process inevitably destroys the host cell but not before the virus has self-assembled enough new particles to infect more living tissue and continue its path of destruction1.1-s2.0-S1879625715000115-gr3

If CNS barriers fail to keep viruses out of the brain, the body mounts a rapid inflammatory response to combat the infection.

 

 

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Microglia, macrophages, lymphocytes (as seen here) and dendritic cells are recruited to the site of injury to clear away dead tissue and remove viral particles.

 

 

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However, some viruses, such as lymphocytic choriomeningitis virus (LCMV), have found ways to become invisible to the body’s innate immune response system.

 

In a recent paper from the McGavern lab at the National Institutes of Health, genetically modified mice were infected with noncytopathic LCMV in order to elucidate innate immune activity in response to CNS viral infection. They utilized microarray analysis of gene regulation and 2-phton imaging to investigate the progression and effects of LCMV. Surprisingly, they found that the production of type 1 interferons (IFN-1) are an essential element in combating viral infection. IFN-1s are part of a non-redundant signaling pathway that induces a protective inflammatory response including activation of microglia and recruitment to the vasculature to clear viral agents2.

Microglia (green) surrounding blood vessel (blue) and astrocytes (red).

IFN-1 signaling is an incredibly important process in fighting viral infection. It is the single orchestrator of innate immune gene expression and there is no redundant mechanism to mount an alternative counter-response. Without the activity of IFN-1, viruses such as LCMV can run rampant in the nervous system, spreading from the meninges to parenchymal astrocytes and oligodendrocytes. Current research is uncovering what neurotropic viruses are affected by IFN-1 signaling. This research has the potential to facilitate the development of therapeutics to modulate anti-viral immunity in the CNS.

To learn more about the nature of viruses and their effect on the global community, come hear Axel Nimmerjahn speak at the CNBC, Tuesday, May 19th at 4:00 pm.

 

  1. Swanson, P. A. 2nd and McGavern D. B. (2015). Viral Diseases of the Central Nervous SystemCurrent Opinion Virology,11;11C:44-45
  2. Nayak, D., Johnson, K. R., Heydari, S., Roth, T. L., Zinselmeyer, B. H., & McGavern, D. B. (2013). Type I Interferon Programs Innate Myeloid Dynamics and Gene Expression in the Virally Infected Nervous System. PLoS Pathogens, 9(5), e1003395.

 

Bankole Aladesuyi is a first-year Neurosciences student currently rotating with Dr. Tom Hnasko. Although a little less worried about common viruses like LCMV, in his spare time he tirelessly labors to find a vaccine for the rare zombies virus.

The Magic School Bus, Tumbleweeds, and Neurodegeneration

With an aging population, the topic of neurodegeneration – in its myriad forms – has quickly come to the fore. These are diseases that have diverse symptoms such as memory loss, confusion, and loss of motor function but only reach a common ground in that they are progressive, terminal, and currently have no cure.

Though if you were able to shrink yourself down, Magic School Bus style, and travel into the brains of these affected individuals you may be surprised by what you see. Splashing along the ventricular river, you float about in the lateral ventricles, before scooching through the foramen of Monro, and sloshing through the cerebral aqueduct. Alright. Recess is over. Let’s see what’s going on here!038976160282_1 (1)

You shoot out of the ventricles into intercellular space. Before you, cells loom like whales in a dark ocean. You see glia wrapping themselves around telephone-pole-sized axons that zig and zag in all directions. Dendrites branch and branch in fractal-like perpetuity. Onto the basal ganglia! The cells here are packed close together. You put on special glasses so you can see them communicating with one another (patent pending). Your face is glued to the window. You are looking aghast at the beauty of this intricate system, when you hear Ms. Frizzle (still single, though definitely available) gasp.

“Would you look at that?”

The whole class crams over to one side of the bus. Inside of one of these beautiful, Moby Dick-sized cells you see what looks, for all the world, like a tumbleweed (but those wordy scientists like to refer to them as neur-o-fib-ril-lary tangles). You’re not sure what it’s doing (that’s okay, neither are most researchers!), but it is clear it is not supposed to be there.

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You take the express route out of this patient’s brain, through the Olfactory tract, past dendrites sticking their legs through the epithelium in the nose just to make contact with pinballing particles floating about. This patient has Parkinson’s disease. Maybe this was a one-time thing. He happened to eat some particularly nasty supermarket sushi and got stuck with tumbleweeds in his brain. That’s plausible, right?

Another day, another patient, another field trip. This one with fronto-temporal dementia. This time you take a circuitous and bumpy route up gyri and down sulci, across fissures, decussating, and re-decussating until you reach your destination. This time you gasp. Those beautiful pyramidal cells with their apical dendrites protruding like antennae, and their prominent somas looking like a giant heart, are filled with tumbleweeds. Ms. Fizzle sighs a long unrelated sigh. She has been persistently trying to record a Snapchat to her newest date, however it seems no one has any good ideas on how to record in the frontal lobe.

Day three of this trilogy of field trips. You were promised a roller coaster. Tintinnabulating off the tympanic membrane, you loop-d-loop around the cochlea, hop-scotch across the basilar membranes, corkscrew up tracts, and nose-dive until you come to the main event: that loop within a loop, the hippocampus. Racing along the cell layer in CA3 you hear a screeching sound. The bus is slowing down. It’s not going to make it! You look out the window and there they are again. Those tumbleweeds, infesting everything.

Ms. Fizzle was “out sick” today (read: her date went well) and you have an incredibly overqualified substitute teacher. Dr. Mel Feany of Harvard University. You ask, “Dr. Feany, we’ve been in three brains of three patients with very different diseases, but every time we’ve seen these tumbleweeds. What are they and how are they messing everything up?”

She looks at you with a coy smile and says:

Come to my talk entitled, Genetic Analysis of Neurodegeneration at 4:00PM in CNCB Marilyn Farquhar Seminar Room and I’ll set you straight. For surely, you don’t know the half of it!”

——-

the IT paper to read by Doctor Feany:

“Tau Promotes Neurodegeneration through Global Chromatin Relaxation”

Image Sources:

http://eshop.me100fun.com.hk/the-magic-school-bus-brain-station.html (Magic School Bus)

adaptation of image from: http://www.brightfocus.org/alzheimers/about/understanding/plaques-and-tangles.html

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This blog post was brought to you by Sage Aronson, a First-Year Graduate Student in the Neurosciences Program at UCSD. An avid lover of wool socks, things with two wheels, and guacamole — Sage has recently joined the lab of Roberto Malinow and is currently interested in electrophysiology and shining lights into brains.

Hippocampal-cortical interactions underlie memory consolidation

“We have, each of us, a life-story, an inner narrative whose continuity, whose sense, is, our lives. It might be said that each of us constructs and lives, a ‘narrative’, and that this narrative is us, our identities … to be ourselves, we must have ourselves … we must recollect ourselves …”

Oliver Sacks, who authored this quote, is a neurologist well known for his compelling essays about his case studies (excerpt from “A matter of identity” in The Man who mistook his wife for a hat, p.105). Here, he is reflecting on his patient Mr. Thompson, who suffers from severe Korsakoff’s syndrome. Anterograde amnesia is a major symptom of Korsakoff’s that prevents Mr. Thompson from forming new memories. Interestingly, Mr. Thompson continually fabricates seemingly genuine memories when talking to others, in an apparent attempt to replace the narrative that he has lost since he obtained amnesia. While reading about Mr. Thompson desperately trying to make sense of his life, the importance of a functional memory in going about our lives is painfully appreciated. Fortunately, labs across the world are researching to understand the mechanics of memory and how it catastrophically fails. Recent insights have come from the lab of Lila Davachi, the upcoming seminar speaker for UCSD neuroscience.

Memory is often understood as having three principal underlying processes: encoding, consolidation, and retrieval. In terms of anterograde amnesia, the pathology may arise from deficits in any of these three processes. First, perceptions must be encoded into the brain as a construct that can be efficiently accessed and computed on. For long-term function, the memories must be consolidated by stabilizing its neural representation, so that the past information can later be recalled (retrieval). A prevalent theory is that memory consolidation occurs as the neocortex and hippocampus communicate in order to establish a hippocampal-independent representation of the thing to be remembered. This idea is well supported, including recent work showing that disruption of hippocampal reactivation during slow wave sleep impairs subsequent memory performance (Girardeau et al, 2009).

Earlier this decade, Lila Davachi’s lab enhanced our knowledge of memory consolidation in humans (Tambini, Ketz, & Davachi, 2010). Using functional magnetic resonance imaging (fMRI), they were able to quantify the interactions between different brain regions by correlating the recorded blood-oxygen-level dependent (BOLD) signals between defined regions of interest (see Figure 1 below). Interestingly, specific cortico-cortical and hippocampal-cortical interactions were predictive of future memory performance.

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Figure 1. During and after an object-face association task, there was an increased correlation between the BOLD responses in the fusiform face area and the lateral occipital complex. (Figure 2A in Tambini, Ketz, & Davachi)

While in the scanner, subjects completed 2 different tasks, which both began and ended with a resting period for potential consolidation. In the first task, the subjects observed object-face pairs, while the second task contained several examples of scenes associated with faces. Importantly, subjects’ performance on subsequent memory tests were significantly different: object-face pairs were recalled more reliably than the scene-face pairs. This is because the scenes are less “distinct” from one another than pairs of objects, and so it is more difficult to make separable associations (to consolidate!).

This contrast in performance is related to the discrepancy in cortico-cortical interactions during the rest periods after each task. After the object-face (OF) association task, the cortical face region (fusiform face area, FFA) and the cortical object region (lateral occipital complex, LO) maintained higher functional connectivity (see Figure 2 below). However, this was not the case after the scene-face (SF) association task, as the correlation between the FFA and the cortical region for scenes (parahippocampal face area, PPA) was unchanged from baseline rest conditions.

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Figure 2. Functional connectivity between relevant cortical regions was increased after the object-face association task, but not the scene-face association task. (Figure 2D in Tambini, Ketz, & Davachi)

 

Tambini, Ketz, and Davachi also quantified functional connectivity between the hippocampus and cortex. They found that the these interactions were also increased only for the rest period immediately following the object-face association task. Together with the behavioral results, these findings support the previously mentioned theory that hippocampal-cortical interactions underlie memory consolidation.

The cross-subject relationship between this functional hippocampal-cortical connectivity and subsequent memory performance is the most exciting finding of this paper. As seen in Figure 3 below, subjects with higher hippocampal-LO correlations following a task tended to retain those memories better.

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Figure 3. Differences among individuals in their subsequent memory performance can be explained by the degree of hippocampal-cortical interactions during the post-task rest period. (Figure 3D in Tambini, Ketz, & Davachi)

 

It is possible that active rehearsal by the subjects could confound the observed effect. Rehearsal would suggest correlations between the prefrontal cortex and the lateral occipital cortex. However, this correlation did not carry information about subsequent memory performance, so rehearsal was likely not confounding.

This is the first time that hippocampal-cortical interactions have been directly implicated in enhancing long-term memory consolidation. Additionally, this study is groundbreaking in that it demonstrates memory consolidation improvements in an awake rest period, as prior studies had only proven this association during sleep periods. Hippocampal-cortical and cortico-cortical correlations had been seen in human resting-state fMRI, but their behavioral relevance was not identified until the current study.

Interested in learning about the more recent developments in our knowledge of memory consolidation? On Tuesday, April 28 at 4pm, Dr. Lila Davachi will give a talk entitled “Behavioral and neural investigations of human memory consolidation”, in the Center for Neural Circuits and Behavior Marilyn C. Farquhar Conference Room.

 

Scott Cole is a first-year Neurosciences student currently rotating with Dr. Eric Halgren. When his memory encoding and retrieval are operational, he likes to compare and critique carne asada burritos across San Diego, which make the optimal post-volleyball meal. You can follow him on Twitter @scottrcole.

Reloading the Synapse

The nervous system is remarkably flexible in dealing with information across different timescales.  We can react to emergencies in milliseconds, yet also store treasured memories for years. How the nervous systems navigates information with various timing demands is a major question in the field of neuroscience.

One of the strategies the nervous system uses to meet these timing challenges is to transmit information in two different ways. Electrical signals give the nervous system speed. Chemical signals give it flexibility to modulate signals across time. These two signals interface at the synapse.

The synapse is the location where one neuron connects to another. When a neuron “fires”, it is transmitting a fast electrical signal down its axon to the synapse at the axon terminal. At the synapse, the fast electrical signal is converted into a slow chemical signal. Neurotransmitters (such as glutamate or dopamine) wait at the axon terminal, where they are packaged into vesicles. When the electrical signal reaches the synapse, the vesicles are triggered to fuse with the membrane and release their contents into the synapse. The neurotransmitter subsequently binds to receptors on the surface of the receiving neuron – effectively transmitting information from one neuron to another. Critically, the chemical signaling is an opportunity for the nervous system to modulate the information being transmitted, such as by switching the amount or type of neurotransmitter available. In 2013, the Nobel Prize in physiology and medicine was awarded to three scientists who discovered the molecular details of how neurons release their packaged vesicles of neurotransmitter.

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Dr. Erik Jorgensen is interested in what happens as the neuron begins to use up its available packages. The stock of packages is finite and most neurons fire quickly enough to use up their stores in a matter of seconds. It is thought that neurons are somehow able to quickly recycle their packages to counter act this problem, but evidence to support this idea has been scant.  Vesicles are too small to observe with a light microscope and neurons fire in about ten milliseconds. Being able to see what is occurring at the synapse over such a short timescale is a difficult task.

To address this question, Jorgensen worked with Dr. Shigeki Watanabe to develop a new technique which gave them the proper temporal and spatial resolution to understand how the neurotransmitter packages were being replenished. First, they introduced a light sensitive molecule to neurons – by turning on and off a light the researchers were able to precisely control when the neuron fired. Then, they would use a high pressure freezer, which could lock all vesicles in place within eight milliseconds. By freezing neurons at different time points, they could effectively create a time-lapse of what the vesicles were doing. Finally, by examining these snapshots of vesicles with an electron microscope, they had the spatial resolving power to examine the vesicles in detail.

Watanabe and Jorgensen found that the vesicles completely fused with the neuron membrane within 30 milliseconds, allowing the vesicles to dump out their neurotransmitter payload into the synapse. Remarkably, in less than 100 milliseconds, portions of the membrane began retracting to be recycled into new vesicles.  This ultra-fast recycling process of vesicles was previously unknown. A slower recycling method was known to take place in some cells, but this mechanism occurred too slowly to meet the demands of neurons. Jorgensen is now following up on this work to discover the details of how neurons are able to perform this ultra-fast feat.

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If you are interested in the details of this work, Katie Fife will be presenting the paper at journal club this Monday, April 20th at 5:30PM in Pacific Hall 3502.

Additionally, Dr. Erik Jorgensen will be giving a talk on this topic titled “Ultrafast endocytosis of synaptic vesicles” this Tuesday, April 21st at 4:00PM for the Neurosciences Graduate Program Dart NeuroScience Seminar Series. The talk is at the Center for Neural Circuits and Behavior (CNCB).


Watanabe S, Rost BR, Camacho-Pérez M, et al. Ultrafast endocytosis at mouse hippocampal synapses. Nature. 2013;504(7479):242-247. doi:10.1038/nature12809.


Peter Osseward is a first-year student in the Neurosciences PhD program at UCSD. He is currently rotating with Dr. Xin Jin. In his spare time, Peter likes to hike and play Ultimate Frisbee.

Galectin-1 (Gal1)ad to Reduce Inflammation after Spinal Cord Injury

Dr. Popovich (who may moonlight as a famous NBA coach on the side or just shares the name) has focused his research efforts on the complex interactions between the immune system and the regrowth and regeneration of the spinal cord after injury. In his lab’s recent work in the journal Molecular and Cellular Neuroscience, Dr. Popovich explores the interaction of Galectin-1 (Gal1) on macrophages and astrocytes at the site of injury in the central nervous system (CNS). As many may already know, a longstanding issue in the field is trying to understand why peripheral nervous tissue (PNS) can regenerate while CNS tissue cannot. Before this paper’s publishing, Gal1 was already known to elicit regeneration in the PNS via its effect on macrophages, but it remained an open question what if any were the effects in the CNS. The Popovich lab took a quantitative approach to answering this question by systematically identifying the expression levels of this protein over a four week time course following spinal cord injury. Interestingly, this work demonstrates a strong upregulation of Gal1 at the injury site as compared to uninjured spinal cord both in macrophages and astrocytes. This suggests a potential target of manipulation going forward in an attempt to tilt the immune-axis in the CNS towards a more conducive environment for regeneration.

Galectin-1 is a switch hitting molecule that changes its activity based on its redox state: when oxidized it is a monomer that acts much like a cytokine, but in its reduced form Gal1 dimerizes and subsequently has a stronger binding affinity with lectins. Of interest to spinal cord injury regeneration is this oxidized monomer form of Gal1, which may assist in axon regrowth. The expression of Gal1 was characterized using the full toolkit of molecular biology (western blots/qRT-PCR) along with immunohistochemistry to determine the localization of Gal1 at various time points. The spinal cord injury was formed using a standard method in which a contusion is applied to the spinal cord of an anesthetized rat.

As stated before, a clear increase in the protein Gal1 monomer at the injury site occurs at the 7 and 14 day time points, which is the height of the inflammatory response in spinal cord injury. mRNA was upregulated at the 3 day time point. Further, protein upregulation was confirmed by quantifying immunoreactivity density. The rest of the paper focuses on the localization and cell specific expression of Gal1. Using fluorescent microscopy, the Popovich lab demonstrates Gal1 up-regulation in macrophages and astrocytes in a series of beautiful images, but furthers the paper by quantifying their images rigorously to provide valuable data on where and when Gal1 is being upregulated in the CNS.
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 Figure 1:A comparison of Gal1 and OX42 a macrophage/microglia marker in uninjured (left) and injured (right) spinal cord. Notice the greater expression of Gal1 in macrophages in the injured tissue in e’ and e’’.

One of the issues raised near the conclusion of the paper is what the monomeric Gal1 is actually doing to macrophages. Gal1 may be reducing these macrophages overall inflammatory response or causing them to differentiate into a “reparative” phenotype. The final two figures of the paper demonstrate one aspect of Gal1 modulatory effect on macrophages: the protein seems to reduce their levels of phagocytosis. Using ED1 as a marker for phagocytic activity, the paper demonstrates a reduction in the colocalization of Gal1 and ED1 at 7 days. Further these macrophages seem to contain less phagocytosed lipids as imaged using the Oil Red O (ORO) stain. Running a linear regression between the coexpression of Gal1 and ORO showed a negative relationship between ORO and Gal1 expression. In sum these figure suggests that Gal1 promotes less phagocytosis in inflammatory macrophages.
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 Figure 2:A comparison of Gal1 and ORO (a marker for phagocytosed lipids). Notice the significant negative correlation between Gal1 immuno- positive cells and ORO density. Conversely, r-t represent a sub-population of macrophages that go against the trend and are highly Gal1+ and have high ORO density.

Overall this paper demonstrates a coherent and quantitative approach to the molecular biology of spinal cord injury, and brings clarity to much of the previous work done on Gal1. By stringently observing time points and quantifying not only expression but cellular localization the Popovich lab has provided a wealth of data on Gal1’s role in the immune response to spinal cord injury. It will be interesting to learn how this data is being used to potentially manipulate the immune response in spinal cord injury, perhaps by increasing Gal1’s expression levels at the injury site.

Marc Marino is a first year Neurosciences student currently rotating with Dr. Roberto Malinow. He is currently enjoying the fact that the San Diego Padres look like they were all injected with a bucket of Gal1 (no longer inflamed and terrible). 


Gaudet AD, Sweet DR, Polinski NK,Guan Z, Popovich PG. Galectin-1 in injured rat spinal cord: Implications for macrophage phagocytosis and neural repair. Molecular and Cellular Neuroscience. 2015; (64):84-94. doi: 10.1016/j.mcn.2014.12.006

Calcium translates between the electrical and chemical languages of the brain

The nervous system is incredibly fast. A dog runs across the street in front of you, and your foot instinctively jumps to the brake pedal. All of this occurs in a fraction of second, often before you even have time to fully process the scene in front of you (was it a dog? or a raccoon?).

The brain is fast because electrical signals in the brain travel fast (over 200 miles and hour, in some cases). And it isn’t just reflexive actions, like slamming your brakes, or lunging to catch a falling glass. We can make complex decisions in the blink of an eye, especially with a little practice and training:

And yet, our decisions and actions, which exist only for a brief instants of time, are captured as memories that can endure for decades.

Electrical signals provide speed, but the brain relies on biochemical reactions as a substrate for slower, more permanent processes like learning and memory. While a discrete electrical impulse typically lasts a few milliseconds, a newly synthesized protein can last for minutes, hours, days, or even years before being degraded. A fundamental question is how these two fundamental languages of the brain — electricity and biochemistry — communicate to each other, despite their widely varying time scales.

Dr. Richard Tsien has been studying this question for many years, and certainly ranks among the very top contributors to this field. His principal insight was that calcium ions participate in both electrical and biochemical signaling, allowing brain cells transmit information from rapid (electrical) signals to slow (chemical) processes that store memories and re-calibrate the system.

Back in 1985, Tsien was the first to characterize the various types of voltage-gated calcium channels [1]. These are proteins that form holes/pores in in the cell’s membrane, and open rapidly in response to an electrical potential. When these channels open, calcium ions (Ca2+) flow into the cell from the extracellular space. These ions carry electrical charge, but also interact with a stupefying number of biochemical pathways that regulate gene expression, protein synthesis and degradation, molecular trafficking, the release of hormones and neurotransmitters, and much more.

Dr. Tsien has tirelessly and meticulously chased down many of these calcium-activated pathways over the years. Far too many to enumerate in this brief summary. But the back-and-forth interplay between electrical and biochemical signaling emerges as a common theme of his work. For example, Tara Thiagarajan, Dr. Tsien, and others [2,3] discovered that chronically blocking electrical activity induces a biochemical response from neurons, causing them to increase the strength of their excitatory connections to other neurons. Calcium plays a pivotal role in this response, as outlined in the flow chart below:

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In this case, calcium works a bit like the thermometer in a thermostat: a drop in calcium signals that activity levels have dropped too low, and turns on the “heat” (excitatory connections between neurons) to compensate. In other work, Tsien and colleagues have studied the calcium-activated pathways that reconfigure synaptic connections to store long-term memories [4], and tune gene expression [5].

Given the privileged position of calcium between the electrical and chemical languages of the brain, it is not surprising that many neuropsychiatric disorders are associated with dysfunction in calcium signaling. For example, progressive memory loss in Alzheimer’s disease is associated with a slow creep in internal calcium levels [6]. Prescribing memantine (Namenda), one of the two approved classes of drugs for Alzheimer’s, can sometimes slow the rate of memory loss by partially blocking calcium ion flow into neurons. While most of Dr. Tsien’s work is focused on unraveling basic biological mechanisms, his lab has also published papers on the role of calcium dysregulation in schitzophrenia, Timothy syndrome, ataxia, and Down Syndrome.

If you are interested in diving into the details of this work, come see Sam Scudder discuss a recent paper from the Tsien lab (6pm, Monday journal club), which examines how calcium signals in distal dendrites regulate gene expression from afar:

Ma et al. γCaMKII Shuttles Ca2+/CaM to the Nucleus to Trigger CREB Phosphorylation and Gene Expression (2014). Cell 159(2): 281-294.

And be sure to stop by CNCB to see Dr. Tsien’s talk on April 7th at 4pm, in the CNCB auditorium.


Alex Williams is a first-year student in the Neurosciences PhD program at UCSD. He applies computational and theoretical techniques to study the molecular mechanisms of neural plasticity and stability. He tweets @ItsNeuronal. Also, Running. Lifting. Burritos.

Do you understand the words that are coming out of my mouth?

I watched Interstellar last month under the stars on campus here at UC San Diego. Several weeks later, the words of this Dylan Thomas poem still resonate within my mind. How is my brain able to understand and remember this spoken message? When I have reached the wise old age of the wonderful Michael Caine, how will my auditory cortex have changed its ability to process these words? Dr. David Woods of UC Davis’s Human Cognitive Neurophysiology Laboratory at the VA Medical Center in Martinez, California is in the business of evaluating age-related changes in speech perception and verbal memory.

Randy Glasbergen, http://glasbergen.com

Age-related decline in verbal processing is in part due to changes in central auditory processing, but little is known of how healthy aging affects the human auditory cortex. Dr. Woods’s laboratory seeks to evaluate age-related changes in speech perception and memory in groups of young and older subjects and correlate them with structural and functional changes in human auditory cortex using several magnetic resonance imaging (MRI) techniques. Dr. Woods proposes to first establish baseline behavioral measures in speech reception thresholds in noise and auditory verbal short-term memory, then investigate age-related changes in auditory cortex surface structure using high-resolution T1-weighted MRI combined with cortical surface mapping. This allows for the analysis of auditory cortex thickness, area, and curvature. Diffusion tensor imaging, an MRI technique used to look at water diffusion, is applied to measure age-related changes in neuropil density and fiber connectivity; this connectivity will then be correlated with prior measured changes in cortical thickness and curvature. Behavioral measures of aging subjects can lend insight into how anatomical changes are associated with behavioral impairment. Lastly, Dr. Woods ties all this structural and behavioral data together with functional organization in the auditory cortex. Using fMRI techniques, he examines the automatic and attention-dependent processing of simple tone stimuli and consonant-vowel-consonant (CVC) syllables. Comparison of tone and CVC processing will elucidate the regions of auditory association cortex that show specific activations to different auditory stimuli and the neural circuits engaged in speech processing.

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Fig. 1 | (A) Cortical surface locations of activation peaks associated with phonological processing of speech sounds from a meta-analysis by Turkeltaub and Coslett (2010). Red dots indicate cortical surface locations of the reported coordinates on the left hemisphere of 60 individual subjects, while blue dots indicate those on the right hemisphere. Cyan cross indicates median location of activations in the left hemisphere; yellow cross indicates that of right. (B) Cortical surface locations of regions responding to CV syllables in comparison to bird song elements, musical instruments, or animal sounds, from Leaver and Rauschecker (2010). See (A) for color associations.

Previous work by Dr. Woods has sought to use population-based cortical-surface analysis of fMRI data to characterize the processing of CVCs and spectrally matched amplitude-modulated noise bursts (AMNBs) in human auditory cortex. Using average auditory cortical field locations (Fig. 1) defined from tonotopic mapping in a previous study, Woods et al. found that activations in the auditory cortex were defined by two stimulus-preference gradients.

Fig. 2 | A schematic map of auditory cortical fields showing stimulus preferences for consonant-vowel-conosonants (CVCs - indicated in ORANGE) and amplitude-modulated noise bursts (AMNBs - indicated in GREEN). The ratio of orange to green reflects relative magnitude of activations with respect to each stimulus type.

Fig. 2 | A schematic map of auditory cortical fields showing stimulus preferences for consonant-vowel-conosonants (CVCs – indicated in ORANGE) and amplitude-modulated noise bursts (AMNBs – indicated in GREEN). The ratio of orange to green reflects relative magnitude of activations with respect to each stimulus type.

Medial belt auditory cortical fields preferred AMNBs (Fig. 2 – green fields), while lateral belt and parabelt fields preferred CVCs (Fig. 2 – orange fields). This preference extends to the core cortical fields, as shown by the medial regions of primary auditory cortex (Fig. 2 – A1) and the rostral field preferring AMNBs and lateral regions preferring CVCs.

Fig. 3 | Quantification of activations by color and brightness. (A) Mean percent signal change of activations coded by brightness. Colors shows stimulus preference (CVC = red, green = AMNB). Yellow regions are activated by both stimuli. (B) Auditory cortical field locations projected onto average curvature map of the superior temporal plane.

Fig. 3 | Quantification of activations by color and brightness. (A) Mean percent signal change of activations coded by brightness. Colors shows stimulus preference (CVC = red, green = AMNB). Yellow regions are activated by both stimuli. (B) Auditory cortical field locations projected onto average curvature map of the superior temporal plane.

Woods, et al. also found a difference in magnitude of activation amongst the different field groups to the two stimuli. Anterior ACFs showed smaller activations (Fig. 3 – dullness of anterior fields), but more clearly defined, singular stimulus preferences (Fig. 3 – only green or red in anterior fields, rather than mixing) than posterior fields (Fig. 3 – note brightness and yellow color in posterior fields).

Fig. 4 | Effects of attention on activation magnitudes in different field groups. UA = unimodal auditory, BA = bimodal auditory attention, BV = bimodal visual attention.

Fig. 4 | Effects of attention on activation magnitudes in different field groups. UA = unimodal auditory, BA = bimodal auditory attention, BV = bimodal visual attention.

Attention significantly enhanced responses throughout the auditory cortex and within every field group, indicating that attentional enhancements to CVCs and AMNBs had similar magnitudes and distributions over auditory cortex (Fig. 4 – mean % of attentional enhancement indicated). This demonstrates that preference gradients are unaffected by auditory attention, which suggests that the preferences of each auditory cortical field reflects automatic rather than attention-dependent processing of difference sound features.

The above investigations are only a brushstroke on Dr. David Woods’s eclectic experimental canvas. To hear more about his adventures in verbal processing and memory, come join in on a lively journal club presentation by Erik Kaestner, a graduate student in the UCSD Neurosciences Graduate Program, on Monday, March 30th at 5:30 PM in Pac Hall 3502. Then, come hear Dr. David Woods himself speak:

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Thanks for listening!

David L. Woods,Timothy J. Herron, Anthony D. Cate, Xiaojian Kang, and E. W. Yund. Phonological Processing in Human Auditory Cortical Fields. Front Hum Neurosci. 2011; 5: 42. Published online 2011 Apr 20. doi:  10.3389/fnhum.2011.00042


Eulanca Y. Liu is a first year graduate student in UCSD Neurosciences and third year in the UCSD Medical Scientist MD/PhD Training Program. When not studying the physiological basis of fMRI in the lab of Dr. Rick Buxton, she enjoys jet-setting, calligraphy and graphic design, opera/theatre/dance, a day at the museum, great single-malt scotch, a hot caffeinated beverage, and dabbling. She will spend the weekend watching the Formula 1 Malaysia Grand Prix whilst writing this post. She can be reached at eyl015 at ucsd.edu and read at medium.com/@ZanzibarByCar. Feedback is welcome!

How is your auditory cortex processing the engine sounds of this Ford Fiesta ST?