Wiring Eyes

We can probably all agree that eyes would just be useless bags of vitreous humor if not for their wiring to the brain.  It’s the highly precise connections between specialized cell types within the eye and their specific target cells within the brain that allow us to visually experience our world.  But how do these axons know where to go during development? How does this intricate architecture contribute to our ability to see and interact with our environment? And how can we reestablish this wiring once it’s damaged by injury or disease? Dr. Andrew Huberman at the University of California, San Diego is hard at work answering these questions.

During graduate school at UC Davis, Dr. Huberman worked on the problem of how initially intermingled left and right eye projections to the lateral geniculate nucleus get segregated during development.  This eye-specific segregation is thought to depend on correlated neural activity patterns.  Using immunotoxic depletion of starburst amacrine cells in order to disrupt correlated firing of neighboring ganglion cells, Dr. Huberman was able to show that left and right eye inputs segregate normally even without this type of correlated firing.  However, when he blocked all spontaneous activity, the projections from the two eyes failed to segregate.

As a postdoctoral fellow with Dr. Ben Barres at Stanford University, Dr. Huberman became captivated by the problem of how functionally distinct subtypes of retinal ganglion cells (RGCs) could be differentially labeled and manipulated.  He began hunting for genetic markers that would allow him to visualize specific subtypes of RGCs.  During a screen to identify mice that selectively express GFP in particular RGC subtypes, he succeeded in identifying a line that expresses GFP only in On-Off direction-selective retinal ganglion cells that detect posterior motion.

Since starting his own laboratory at UCSD, Dr. Huberman has continued to make important contributions to our understanding of the development and organization of the visual system.  Along with a graduate student in his laboratory, Jessica Osterhout, Dr. Huberman has recently contributed to our understanding of how neurons identify their precise synaptic targets.  They have shown that the cell adhesion molecule, cadherin-6, is expressed in a subset of RGCs as well as in their postsynaptic targets in the brain.

In order to establish that this cell adhesion molecule is necessary for these highly precise connections to form, they demonstrated that in a cadherin-6 knockout mouse, these RGCs fail to reach their proper targets and instead innervate other visual nuclei in the brain.

The Huberman laboratory is also interested in how the visual systems of different species is customized for specialized behaviors or environmental niches.  Along with postdoctoral fellow Dr. Olivia Mullins, Dr. Huberman is using electrophysiology along with high-speed video monitoring in order to study the prey capture behavior of the cuttlefish.

Fun fact: Dr. Huberman is also the treasurer and secretary of a non-profit organization, called Board Rescue, that supplies skateboards and safety equipment to low income children.

Cool!

Come join us on Tuesday, May 14^th in the CNCB large conference room to hear more from Dr. Andrew Huberman about his work to understand the development, architecture, and function of the visual system.

Andrea Hartzell is a first-year graduate student in the UCSD Neuroscience Program.  She works in the laboratory of Dr. Brenda Bloodgood.

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Huberman A.D., Wang G.Y., Liets L.C., Collins O.A., Chapman B. & Chalupa L.M. (2003). Eye-specific retinogeniculate segregation independent of normal neuronal activity., Science (New York, N.Y.), 300 (5621) 994-998. DOI: 10.1126/science.1080694

Huberman A.D., Wei W., Elstrott J., Stafford B.K., Feller M.B. & Barres B.A. (2009). Genetic Identification of an On-Off Direction- Selective Retinal Ganglion Cell Subtype Reveals a Layer-Specific Subcortical Map of Posterior Motion, Neuron, 62 (3) 327-334. DOI: 10.1016/j.neuron.2009.04.014

Osterhout J., Josten N., Yamada J., Pan F., Wu S.W., Nguyen P., Panagiotakos G., Inoue Y., Egusa S. & Volgyi B. & (2011). Cadherin-6 Mediates Axon-Target Matching in a Non-Image-Forming Visual Circuit, Neuron, 71 (4) 632-639. DOI: 10.1016/j.neuron.2011.07.006

Dissecting circuits: Bridging the gap from circuits to behavior

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Chalasani S.H., Chronis N., Tsunozaki M., Gray J.M., Ramot D., Goodman M.B. & Bargmann C.I. (2007). Dissecting a circuit for olfactory behaviour in Caenorhabditis elegans, Nature, 450 (7166) 63-70. DOI: 10.1038/nature06292

Functional Compartmentalization and Viewpoint Generalization Within the Macaque Face-Processing System

One thing humans, and in fact all primates can do with remarkable ease compared to computers is face recognition, especially across a range of viewing conditions.  At her lab at Caltech, Doris Tsao tries to explore the way the brain does this.  In her recent Science paper, she explored view invariance in the recently discovered face-processing network of the macaque monkey.  Of the six interconnected face-selective regions, They recorded from the two middle patches (ML, middle lateral, and MF, middle fundus) and two anterior patches (AL, anterior lateral, and AM, anterior medial) while showing images of faces subject to accidental image transformations like changes in view direction.  They found that the anatomical position of a face patch was associated with a unique functional identity: Face patches differed qualitatively in how they represented identity across head orientations. Neurons in ML and MF were view-specific; neurons in AL were tuned to identity mirror-symetrically across views, thus achieving partial view invariance; and neurons in AM, the most anterior face patch, achieved almost full view invariance.

Here they demonstrate the view dependence of  ML/MF and AL and the view independence of AM by showing the separability in MDS space.  Since all the views are overlapping in plot C, the responses to images of each viewpoint are indistinguishable indicating that AM is viewpoint invariant.  They quantify this in G and H using sharpness of identity tuning half-widths and head orientation tuning depths.  Interestingly, the time course of the view invariant response of AM is significantly longer than that of ML/MF and AL.  This indicates that the view invariance is a consequence of a much more complex network structure than simple feed forward connections.

Join us on Tuesday April 30^th, 2013 at 4pm in the CNCB large conference room to hear more from Dr. Doris Tsao about how her group has demonstrated view invariance in the hierarchy of the facial recognition system in macaques.

Marvin Thielk is a first-year student in the UCSD Neuroscience program.  He is currently rotating in the Sharpee lab. 

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Freiwald W.A. & Tsao D.Y. (2010). Functional Compartmentalization and Viewpoint Generalization Within the Macaque Face-Processing System, Science, 330 (6005) 845-851. DOI: 10.1126/science.1194908

Vision is complex: Predicting responses despite non-linearity and heterogeneity

What does it mean to understand vision?  Can we know how the retina will react when we see our favorite painting or our best friend before it even happens?  The work of Dr. Fred Rieke hopes to do just that.  Not unsurprisingly, vision is complex and studying it is hard.  The output neurons of the retina, retinal ganglion cells (RGCs), integrate the incoming sensory information non-linearly and not every piece of incoming input carries the same value (heterogeneity).  These two characteristics make predicting the way the retina will behave to new images quite difficult.  However in their recent work, Dr. Rieke and his team at University of Washington have sought to overcome these challenges and develop a predictive model of how different spatial patterns produce their unique retinal output.

In their study of the retina, the Rieke team focused on one particular RGC, a large, contrast specific cell that they term the ‘On-Alpha-like RGC’.  In response to textured stimuli these cells did show the expected non-linear summation and heterogeneity that make the formation of predictive models difficult (Fig1).

 

Figure 1. On-Alpha-like RGCs respond non-linearly to different texture stimuli and showed responses to rotation of the stimuli, characteristic of heterogeneity

 

In order to develop a predictive model that successfully accounted for these two RGC characteristics, the group turned to looking at the anatomical input to their On-Alpha-like RGCs.  Through careful physiology, they were able to determine that the non-linearity and heterogeneity resulted from the excitatory input that the RGC received from the upstream bipolar cells.  Following this conclusion, Rieke and his team conducted some impressive quadruple labeling studies that allowed them to specify this incoming excitatory input almost exclusively to Type 6 Bipolar Cells (Fig 2).

 

Figure 2. Quadruple labeling identifying Type 6 bipolar cells as contributing over 70% of the excitatory input to On-Alpha-like RGCs

 

To confirm that these Type 6 bipolar cells conferred the non-linearity seen in the On-Alpha-like RGC, Rieke’s group attempted to directly observe a non-linear summation of input.  By presenting two independent light spots, they were able to induce a RGC response from each spot.  In cases where these two spots were presented far away from each other (50µm), the RGC exhibited a normal integrated response summating the effect of each stimulus.  However when presented close together in space (20µm), the RGC response was greater than the sum of the two independent stimuli; the expected non-linear response (Fig 3).  In the case where stimuli were presented close together, the two light spots were presumed to both fall within the receptive field of a single Type 6 bipolar cell (a size consistent with the known receptive field size of Type 6 bipolar cells).  Yet when they were far apart, each spot would activate different bipolar cells, eliminating the non-linear response.  These findings helped to verify that indeed it is the Type 6 bipolar cells which confer the non-linearity to On-Alpha-like RGCs.

 

Figure 3. Two light stimuli presented within a Type 6 bipolar cell’s receptive field produce a non-linear response in On-Alpha-like RGCs whereas stimuli presented far apart, outside of a single receptive field are integrated linearly.

 

 

Based on these findings, Rieke and his team proceeded to construct a more accurate model of how On-Alpha-like RGCs respond to stimuli.  By focusing on the newly acquired information on Type 6 bipolar cell input, they hoped to develop a simpler, more anatomically based model based on the connections between bipolar cells and retinal ganglion cells.  They hoped that this simplified approach could account for the non-linearity and heterogeneity that had previously presented a stumbling block to predictive models.  Indeed, this focus on anatomical connections produced a model many orders of magnitude more accurate than other previous attempts bringing us closer to truly understanding the way the retina processes our complex visual world.

 

Join us on Tuesday April 16^th, 2013 at 4pm in the CNCB large conference room to hear more from Dr. Fred Rieke about how his group has found simplified solutions to complex problems in predicting retinal responses.

 

Geoffrey Diehl is a first-year student in the UCSD Neuroscience program.  He is currently rotating in the Leutgeb lab. 

 

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Schwartz G.W., Okawa H., Dunn F.A., Morgan J.L., Kerschensteiner D., Wong R.O. & Rieke F. (2012). The spatial structure of a nonlinear receptive field, Nature Neuroscience, 15 (11) 1572-1580. DOI: 10.1038/nn.3225

Wave makers: The origins of corticothalamic slow oscillations

It might come as a surprise that while you’re asleep or at rest your neurons do not enjoy a similar period of tranquil inactivity, but instead remain hard at work. In fact, previous studies report that coordinated waves of slow oscillatory activity (< 1 Hz) spread through the cortex and thalamus during sleep, waking rest or under anesthesia. But until now, the mechanisms by which neurons synchronously communicate over such vast distances have been somewhat of a mystery. In their study recently published in Neuron, researchers from Arthur Konnerth’s lab at Technical University Munich probed the origins and trajectory of these slow waves.

Using an innovative approach that integrates optogenetic stimulation with fluorescent calcium recordings in living mice, Konnerth and colleagues discovered several novel properties of slow oscillatory activity. They first virally transduced cortical layer 5 neurons to express channelrhodopsin (ChR2), a light-gated ion channel that selectively activates the genetically-targeted neurons upon light stimulation. They then used two-fiber recordings to optically stimulate the ChR2-expressing cells and simultaneously record calcium signals associated with slow waves.

Konnerth’s group demonstrated that spontaneous, as well as visually- and optogenetically-evoked, calcium waves share similar response properties, and showed for the first time that these slow waves behave in an all-or-none manner. Above a minimum threshold of stimulus duration and intensity, slow waves exhibit constant amplitude and duration (Figure 1, left), suggesting that a similar number of neurons typically contribute to the generation of a slow wave. They further revealed that slow oscillations, much like their little brother the action potential, have reliable absolute (< 1.5 sec) and relative (1.5-3 sec) refractory periods (Figure 1, right).

Figure 1. Left: Calcium waves evoked by optogenetic stimulation of various pulse lengths show similar amplitude and duration. Right: Calcium waves demonstrate absolute and relative refractory periods. (Stroh et al., 2013)

But how do these remarkably uniform slow oscillations spread throughout the brain? Notably, the researchers observed calcium waves that propagate throughout the entire cortex and even across hemispheres. While spontaneous waves preferentially traveled from frontal to visual cortex, visually or optically stimulating the visual cortex produced slow oscillatory activity that originated in visual cortex and propagated (at a rate of 22 mm / sec) to bilateral frontal cortex (Figure 2). Thus, locally generated slow waves can rapidly recruit even remote brain regions.

Figure 2. Optogenetic stimulation of the visual cortex elicits calcium waves that travel from visual to bilateral frontal cortex. (Stroh et al., 2013)

Finally, they probed the spatiotemporal dynamics of slow waves that engage both cortical and thalamic networks. Stimulating the lateral geniculate nucleus of the thalamus generated slow oscillations that originated not at the thalamic stimulation site, but in the visual cortex, and proceeded to spread back to the thalamus (Figure 3). Since they did not observe an early thalamic slow wave, this suggests that although the initial thalamic activation is insufficient to produce a local slow wave, it’s adequate to recruit a larger population of cortical neurons that ultimately induces widespread corticothalamic slow oscillations.

Figure 3. Calcium waves induced by optogenetic stimulation of the thalamus (dLGN) appear first in the visual cortex, and later in the thalamus (dLGN, VPM). (Stroh et al., 2013)

Want to learn more about the origins of corticothalamic slow waves? Don’t miss Dr. Arthur Konnerth’s seminar, Tuesday April 9, 2013 at 4pm in the CNCB large conference room. Your slow wave activity is sure to be at an all-time low during this stimulating talk!

Emilie Reas is a fourth year UCSD Neurosciences PhD student working with Dr. James Brewer. She has an unhealthy obsession with the hippocampus and uses fMRI to study human memory.

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Stroh A., Adelsberger H., Groh A., Rühlmann C., Fischer S., Schierloh A., Deisseroth K. & Konnerth A. (2013). Making Waves: Initiation and Propagation of Corticothalamic Ca2+ Waves In Vivo, Neuron, 77 (6) 1136-1150. DOI: 10.1016/j.neuron.2013.01.031

Arthropods: More than just a pretty face, they have brains that can preserve for over half a billion years

Behold the Arthropods. They are invertebrates with exoskeletons, segmented bodies and jointed appendages (examples: insects, arachnids, crustaceans). Exquisitely versatile and adaptable, they comprise the most species-rich phylum and they’ve been around since at least the early Cambrian Period (541-485.4 million years ago, (Mya)). Look where you’re standing. Chances are that an athropod’s already been there and moved on. In this week’s Seminar Series installment we will hear from Dr. Nicolas Strausfeld whose pioneering work grapples with the enormous systematics challenge of reconstructing the evolutionary history and taxanomic connections within the Arthropod group. Dr. Strausfeld and his group investigate and infer phylogenetic relationships through analyses and comparisons of brain architectures, a method developed in his lab (a.k.a. neural cladistics). This method has greatly contributed to the molecular and morphological phylogenetic toolkit because neural architectures are highly conserved within related groups. Thus, relationships between taxa can be inferred through evaluation of the presence/absence of defined neural structures and with the aid of computational tools. These studies lend tremendous insight into whether neural arrangments in existing animals are novel or ancestral and reveal the story of how brain structures have evolved – or not if structures have proven adaptable – through time.

Dorsoventrally flattened Fuxianhuia protensa from the Chengjiang Lagerstatte. Dorsal view of complete specimen, YKLP (Yunnan Key Laboratory for Paleobiology) 11321.

One of the problems limiting neural cladistics is that fossilized arthropods rarely preserve neural tissue. However, recently Dr. Strasfeld’s group characterized Fuxianhuia protensa, a stem-group arthropod with exceptional brain and internal organ preservation.These studies were done from ~50 specimen at the Yunnan Key Laboratory for Paleoneurobiology (YKLP) in the Yunnan Province, Southwest China. Remarkably, Fuxianhuia shares brain organization features with many existing arthropods, including malacostracans (one of the six classes of crustaceans) and insects. These findings clarify long-disputed phylogenetic relationships and suggest that the sophisticated brains of extant arthropods had an early origin. This is consistent with the idea that the compound eyes supporting these resilient nervous systems have also retained much of their structure and resolution throughout this great expanse of geologic time.

Reconstructed brains of Fuxianhuia protensa and land hermit crab Coenobita clypeatus showing homologies with Malacostraca. They share 3 nested neuropils in each eye stalk (arrowed 1-3), A1n (antennal nerve), A2n (second pair of nerve roots), op t (optic tract).

Is your scientific appetite whetted yet? Then come to the center for neural circuits and behavior this Tuesday April 2, 2013 at 4pm to learn more from Dr. Strausfeld’s himself.

Wilmer Del Cid is a first year graduate student in the UCSD Neuroscience Program. In the spring he will rotate in the lab of Dr. Ulrich Mueller where he will study the development of the cortex. 

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Ma X., Hou X., Edgecombe G.D. & Strausfeld N.J. (2012). Complex brain and optic lobes in an early Cambrian arthropod, Nature, 490 (7419) 258-261. DOI: 10.1038/nature11495

Launching our new “Spikes in the Classroom” outreach module

We are incredibly excited to announce the addition of a new module to our outreach efforts. When we go into a classroom, we bring plenty of dead brains… rare jarred brains of porpoises and penguins for our “Comparative Anatomy” module and squishy sheep’s brains that students get to touch and hold and inspect (always with a gloved hand!). However, most neuroscientists work with living brains: brains with neurons firing and sucking up oxygen from the blood. But it has always proven difficult to to bring this kind of neuroscience into the high-school classroom. Until now.

We are going to be bringing real spiking neurons into classrooms throughout San Diego with our new “Spikes in the Classroom” module, thanks to a grant from the Brain Corporation, an awesome set of equipment by Backyard Brains, and some hard work from our outreach team. Now, students will have the chance to listen to actual spiking neurons, see them on an iPad, and record from them like real neuroscientists. We’ll be introducing them to the fundamentals of neurophysiology, sensory encoding, and neural prostheses.

The key to all of this is the incredible work of Tim & Greg at Backyard Brains, who have been living up to their motto of “Neuroscience for Everyone!” by introducing their electrophysiology rig known as the “Spikerbox,” which lets anyone record neurons from a cockroach for under $100.

We’ve had a spikerbox for a while. In fact, we were the proud recipients of the first ever production SpikerBox. In 2010, Tim & Greg expedited their production line in order to get us our SpikerBox in time for that year’s San Diego Science Festival. The thing was an alpha-release beauty, with a balsa wood enclosure, custom glitter pen graphics, and a slight buzz that we just couldn’t get rid of. We were living at the bleeding edge and this SpikerBox was a bit too “buggy” for our school visits. Thanks to the Brain Corporation grant, we’ve been able to update our equipment to the latest release of the Backyard Brains Spikerbox, start a colony of roaches for experimental subjects, and we’ll be able to bring an iPad to our classroom visits so students can see and record from real neurons. Graduate student Erik Kaestner says, ‘the best part of the demonstration is when you can show neural control of muscles by using a student’s ipod to inject electricity into the neurons and have the cockroach leg dance along to any song with enough bass’.

We’ve run our new module at one school and at the San Diego Science Festival and in more of our school visits in the coming months. If you would like the UCSD Neurosciences Outreach Program to visit your school, contact Stephanie Alfonso at salfonso@ucsd.edu.

Carla Shatz: An Inspiration for Women in Neuroscience

Dr. Carla Shatz is a woman of many firsts.

She began her career in neuroscience as the first undergraduate student of Drs. David Hubel and Torsten Wiesel of Harvard Medical School (yes, that Hubel and Wiesel who won the Nobel Prize for their work on the visual system in 1981).  After graduating from Radcliffe College with a B.A. in Chemistry in 1969, she received a Marshall Scholarship to study at University of College London.  There, she received an M.Phil in Physiology and then returned to Harvard to continue her work with Hubel and Wiesel.  In 1976, she was the first woman to receive a Ph.D. in Neurobiology from Harvard Medical School.

As a postdoc, she spent a couple of years working with Dr. Pasko Rakic until she left for Stanford University in 1978.  She was the first woman to receive tenture in the basic sciences at Stanford and the first woman to be hired by the Stanford School of Medicine.

From there, she moved to UC Berkeley and then to Harvard (where she was Chair of the Department of Neurobiology), and returned to Stanford in 2007.  Still at Stanford, in addition to running her lab, she directs The Bio-X Initiative, an interdisciplinary program that unites engineering, computer science, physics, and chemistry with traditional biology and medicine, to study the complexity and find solutions to critical problems of the human body.

Dr. Shatz’s lab uses numerous methods, including molecular biology, slice physiology, behavior, and in vivo imaging, to study the development of the mammalian visual system.  A recent paper, “Neuroprotection from Stroke in the Absence of MHCI or PirB,” illustrates how her lab’s work expands well beyond the development of the visual system.  The paper applies the lab’s previous findings—that molecules important for the body’s immune response: major compatibility class I (MCHI) and its receptor, paired immunoglobulin-like receptor B (PirB), impair plasticity in the hippocampus and visual system—to the problem of recovery after stroke.  In this study, they use mice lacking two MCHI genes (H2-Kb and H2-Db) and mice lacking PirB, mimicking stroke with transient middle cerebral artery occlusion (MCAO) in vivo and oxygen glucose deprivation (OGD) in vitro.  After observing greater recovery in the KbDb and PirB knock-out mice, they suggest the development of therapies to target MCHI and PirB to improve recovery following stroke.

To learn more from this pioneering neuroscientist, please join us on Tuesday, March 19, 2013, at 4:00pm in the Large Conference Room of the Center for Neural Circuits & Behavior for Dr. Shatz’s talk, “Brain circuit tuning during developmental critical periods.” 

Fun fact: Dr. Shatz was the postdoc advisor to two 2012-2013 Neurosciences Seminar Series speakers, Marla Feller and the unfortunately ill and absent, Sue McConnell!

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Adelson J., Barreto G., Xu L., Kim T., Brott B., Ouyang Y.B., Naserke T., Djurisic M., Xiong X. & Shatz C. & (2012). Neuroprotection from Stroke in the Absence of MHCI or PirB, Neuron, 73 (6) 1100-1107. DOI: 10.1016/j.neuron.2012.01.020

Primate Visual Space: The Entorhinal Frontier

Throughout the course of human history, great metaphorical emphasis has been placed on developing an understanding of our “place in the world.”  Although this proverbial construct refers more to a sense of self-efficacy, it underscores the inarguable importance of determining our position as it relates to the environment around us in producing proper behavior.  Research into the neural mechanisms underlying determination of place in relation to the individual has successfully delved into this field (no pun intended), elucidating an essential role for specific cell types in the hippocampal formation in processing location-relevant information.

Despite comprehensive research revealing the existence of place and grid cells expressly for this purpose in rodent models, the mechanisms of location-dependent learning and memory in the primate brain remain somewhat of a mystery.  Thankfully, Dr. Elizabeth Buffalo and colleagues at Emory University have committed to tackling this very question.

Dr. Elizabeth Buffalo

In a recent paper, Buffalo and colleagues explored spatial representation in the monkey entorhinal cortex and hippocampus using a free-viewing visual memory task called the visual preferential looking task (VPLT).  Monkeys were head fixed in a chair and shown novel images of fixed reference frame, twice each, on a computer monitor.   A laminar electrode array was placed in the entorhinal cortex of each of the three monkeys in order to record spikes and local field potentials, allowing for recording from a total of 342 neurons in the hippocampal formation.  Data regarding gaze location was determined using an infrared eye-tracking system (ISCAN).

The authors found that the monkeys explored the images, which consisted of various complex elements including abstract art, landscapes, animals, and people, with a series of dynamic fixations that coincided with increases in entorhinal cortical firing.  Conversely, changes in hippocampal activity were significantly more variable.

Fig 1. Examples of images shown and imposed representations of dynamic fixations

Buffalo and colleagues identified these cells as grid cells, and observed a significant presence in the posterior EC, but not in the hippocampus. Moreover, grid cells were located in both superficial and deep layers of EC, indicating a possible role in processing both input and output from the hippocampus. These results were consistent with expectations based on analogous structures and projections in the rodent brain (i.e. MEC). The authors  additionally concluded that firing rate was independent of stimulus content.

Fig 2. (Middle) Grid cells and Border cells both qualitatively identified in primate EC.  (Right, Left) Autocorrelations for representative cells of these types.

Buffalo and colleagues also noted a significant population of EC neurons that increased firing rate when gaze was located near the edges of stimuli. Qualitatively, these cells matched the border score criteria used in rodents to identify border cells, indicating that the visual map of space in the entorhinal cortex of primates may similarly be anchored in a framework in which the boundaries of stimuli serve as landmarks.

Fig 3. Reductions in firing upon exposure to repeated stimuli were more pronounced in anterior regions of EC (grey areas demarcate significant response reductions)

At more anterior locations, larger proportions of visually responsive neurons showed a reduction in firing rate in response to repeated stimuli, with the magnitude of this reduction increasing progressively, insinuating a role in recognition memory. This memory-linked response seemed to be independent of spatial representation, as while grid cells in anterior regions of EC were more likely to produce a memory response, the density of this cell type gradually declined along the anterior axis, with absolutely no grid cells located beyond the posterior 50% of the EC.

Fig 4. The probability of spiking for EC grid cells phase locked to theta oscillations

Finally, Buffalo and colleagues determined that the EC exhibited theta oscillations that resemble those found in primate hippocampus, and that the grid cells appeared phase-locked with the trough of the local field potential theta, suggesting theta modulation of these cells. However, firing rate maps of grid cells during theta and non-theta bouts did not appear significantly different, requiring adaptation of the current grid cell models basing their function on interactions of oscillations.

Buffalo’s work is both fascinating and imperative for attaining a fundamental understanding of primate learning and memory. In addition to detailing and localizing the qualities of the primate visual map, Buffalo confirmed that primates can form spatial representations during visual exploration at a distance. This discussion barely scratches the surface of the incredible work the Buffalo lab performs to elucidate the complexities of learning and memory in primates. Join us on Tuesday, March 5th at 4 pm in the CNCB auditorium for what is sure to be an informative and exciting lecture by Dr. Elizabeth Buffalo!

Landon Klein is a first year in the UCSD Neurosciences Graduate Program.  He possesses a strong interest in drug mechanisms and addiction.  Landon is currently rotating in the Alcoholism and Addiction Lab of Dr. Marisa Roberto at The Scripps Research Institute.  Landon insisted on eating buffalo chicken last night in honor of Dr. Buffalo’s groundbreaking work.

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Killian N.J., Jutras M.J. & Buffalo E.A. (2012). A map of visual space in the primate entorhinal cortex, Nature, DOI: 10.1038/nature11587

A brave foray into the daunting complexity of the human cortex

Understanding the organization of human cortex has proven to be more difficult than examining that of other animals. For instance, we are more limited in the methods we can use to investigate human cortical networks. Brodmann attempted to classify and name human cerebral cortex by studying the cytoarchitecture of post-mortem brains; his legacy was a nomenclature defining 50-odd regions and the bane of neuroscience undergrads everywhere. While his system is useful in providing a way to communicate about the same brain regions, it is important to note that it offers no insight into how these areas are interconnected or functionally related. Modern human neuroimaging techniques can provide some insight into the functional connectivity in the human brain and the complex behaviors and states that different cortical networks subserve.

Randy Buckner and colleagues use functional connectivity MRI (fcMRI) to investigate the intrinsic networks within human cortex. They aimed to create reference maps that would best describe the functional connectivity of human cortex and how distributed cortical networks are organized. FcMRI measures intrinsic functional relationships between brain regions using temporal correlations in regional activation. Of course, it is important to note that fcMRI provides no information about the directionality of the connections and is influenced by the prior history of activation.

The investigators collected data from 1000 subjects that were run in a 3T MR scanner. They used 3 different passive state tasks: eyes open (EOR), eyes closed (ECR), and fixation (FIX). The authors also used visual stimulation to map out the retinotopic maps of early visual areas. In their first set of analyses, they used a clustering algorithm to segregate the cortex into functionally coupled areas. They identified 7 and 17 (in a finer analysis) networks.  In subsequent analyses, they examined the organization of sensory, motor, and association cortex networks.

Buckner and colleagues found that the functional properties lower-level sensory and motor networks were very different from higher-order association cortices. Sensory and motor networks were organized in a topographic and hierarchical way and tended to form local networks. On the other hand, higher-order association areas were organized into multiple, distributed networks without a clear hierarchy.

Specifically, for visual cortex, the authors found that early visual areas were strongly coupled to each other and only very weakly coupled to areas outside of the visual cortex. They also found that they could divide the areas in early visual cortex, such as V1-3 and V4v, into central and peripheral components (using a visual stimulation task to map out retinotopy).

Buckner and colleagues then found that the somatomotor network, like visual areas, comprises of preferentially locally coupled areas organized in a clear hierarchy (see Figure 29). Figure 29 examines this network and explores several possibilities for its hierarchical organization (with B and C being the best arrangements). These analyses demonstrated the presence of a dorsoventral division of the somatomotor network. The authors postulated that this division might reflect the boundary between the face and body representations in cortex. Moreover, the authors chose to focus on the functional connectivity between areas MT+ and aMT+ and parietal and frontal association areas. Figure 25B shows the correlations between areas MT+ and aMT+ and 4 visual, 4 parietal, and 2 frontal regions. Buckner and colleagues found that activation in MT+ had a stronger correlation with early visual cortex relative to aMT+, while aMT+ had stronger correlations with parietal and frontal regions.

Figure 29. Functional connectivity estimates of the hierarchical organization of a canonical sensory-motor pathway. A: 6 seed regions arranged into a 5-level functional hierarchy using the replication data set. B and C: 2 best hierarchical arrangements of the seed regions as measured by the proportional of violated hierarchical and lateral constraints. A violation occurred when the ordering placed more strongly correlated regions farther apart in the hierarchy than more weakly correlated regions. D and E: 2 poor hierarchical arrangements of the seed regions as measured by the proportion of violated hierarchical and lateral contraints. Relative ordering of the seed regions (A and B) within the functional hierarchy agrees well with the proposed macaque visual hierarchy.

Figure 25. A: 4 visual, 4 parietal, and 2 frontal seed regions were used to quantify the functional coupling of aMT+ and MT+ to distributed cortical regions. B. polar plots of MT+ (blue) and aMT+ (red) connectivity with the visual, parietal, and frontal seed regions were computed using the replication data set. (from Yeo et al., 2011).

In higher-order association cortex, the authors found multiple, parallel distributed networks (7 and 17). Among the networks they discovered, the dorsal/ventral attention networks, the frontoparietal control network, and the default network were analyzed in greater depth. Buckner and colleagues generally found that these large scale, distributed networks were interdigitated and presented evidence for some cross-talk between these networks.

Please join us on Tuesday Feb. 19 as Randy Buckner discusses how modern MRI methods can illuminate the intrinsic functional connectivity of human cerebral cortex. Seminar will be held in the CNCB Large Conference Room at 4pm.

Laura Sancho is a first-year neuroscience PhD. student currently rotating in Jeff Isaacson’s lab. She enjoys systems neuroscience and electrophysiology. 

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Thomas Yeo B.T., Krienen F.M., Sepulcre J., Sabuncu M.R., Lashkari D., Hollinshead M., Roffman J.L., Smoller J.W., Zollei L. & Polimeni J.R. & (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity, Journal of Neurophysiology, 106 (3) 1125-1165. DOI: 10.1152/jn.00338.2011