Neuronal memory

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Primary reference:

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

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

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

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

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

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

Sexual Dimorphism Found in Olfactory Processing Circuit

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

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

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

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

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

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

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

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

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

Also:

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

The thalamus coordinates your attention

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

fMRI as an unprocessed movie of the mind

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

That’d be cool.

Neural decoding Neuron issue- notably published after (1)

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

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

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

RDPs

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

Image

Brain region vs. accuracy in the two coherence conditions

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

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

Image

2008 model for identifying natural scenes (2)

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

Image

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

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

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

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

Cite

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

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

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

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

Making Me Hungry

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

COOKIES!!!

COOKIES!!!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References:

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

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

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

“UFO – Unidentified Form that looks a lot like Obama,” or “How do we decide?”

obama-golf-shoes

Pedestrian Barack Obama

As I take my cat for a stroll, I see a familiar form in the distance.  Is the form I’m approaching Obama?  Let’s forget about the low likelihood of passing Obama in San Diego and the myopia that cripples my ability to see far distances and go through a scenario of what might be going through my head as I make this judgment.  Over time. I am accruing sensory information that either sways me to say, “well gee wilikers, that’s Obama!” or, conversely, “aww shucks, that’s not Obama.”  According to the drift-diffusion model of decision-making, as I accrue evidence that the form is Obama, an internal likelihood running count is shifted towards the Obama decision, and as I accrue evidence against that scenario, it is shift towards the “some random person” decision.  Thus, at any moment, I have to maintain a memory of the evidence I have already gathered and add evidence to that memory.  At some point I reach a decision threshold at which enough cues have added up in one direction that I feel confident with making a decision.  If this is real life, I have set my threshold much too low and impulsively yell, “Heyo Bama!” only to realize within the next 20 steps that I have been approaching a tree stump.

We make numerous judgments like these within a day, some of which are incorrect.  There are two main possibilities that could underlie this imperfection.  The first is that the sensory inputs one receives are imperfect either due to the inherent noisiness of the stimuli, the variability introduced during sensory processing in the brain, or the imperfection of adding the evidence to the estimate kept in memory.  The second is that the memory of the signals one has received is noisy and drifts with time.  Dr. Carlos Brody’s lab at Princeton University (Brunton et al., 2013) set out to distinguish between these two possibilities using both rats and humans.  Subjects were asked if they heard more tone clicks on the right or left side in an auditory discrimination task in which tone clicks were presented at random time intervals but with a fixed overall rate.

Capture

Mr. Rat, are there more clicks on the left side or the right side?

The authors modeled each subject’s responses using nine parameters, two of which were sensory noise and memory diffusion noise.  Sensory noise will add uninformative variance proportional to the sum of the amplitude of the tone clicks and memory diffusion noise will add variance proportional to the stimulus duration.  Thus the variable timing allows the two possible noise sources to be pulled apart.  By using best fit models given the data of each subject, Brody et al. found that in 13 out of 19 rats and for all of the human subjects (n=3), the best fit value for the memory diffusion noise was zero, implying that the subjects could maintain evidence perfectly and thus that decision making imperfections stem from variability in sensory processing or in adding evidence to the estimate.

Capture2

Figure 2. The model can fit a variety of mechanisms, but the data consistently fit to a pulse-accumulating mechanism with zero noise in the accumulator’s memory.
(A) Three examples of the mechanisms that the model can represent. Top: The ideal, a pulse accumulator that weights all pulses equally. Middle: A burst detector. If three or more pulses from the same side arrive within 50 ms, the sticky bounds are reached, meaning that a commitment to orient to that side is made. Bottom: A precedence detector. If pulses from one side tend to arrive shortly before pulses from the other side, the adaptation minimizes the second side’s pulses, and the decision tends toward the preceding side. (From Brunton et al., 2013)

Please join us in the CNCB Large Conference room on Tuesday, January 28th at 4:00pm to learn more from Dr. Carlos Brody about “Neural substrates of decision-making in rats.”

Margot Wohl is a first year student in the UCSD Neuroscience Graduate Program.  She is currently rotating in the laboratory of Dr. Jeffry Isaacson.

Brunton B.W., Botvinick M.M. & Brody C.D. (2013). Rats and Humans Can Optimally Accumulate Evidence for Decision-Making, Science, 340 (6128) 95-98. DOI:

The Informatics of Brain Mapping – On Our Mind

UCSDNeuro on YouTube!

UC San Diego Department of Neurosciences Chair Dr. Bill Mobley and USC’s Dr. Arthur Toga discuss brain mapping, big data (from the molecule to the mind), individual brain configurations, “personalized education,” and more!  From the UCTV series “On Our Mind” on The Brain Channel, powered by the UCSD Department of Neurosciences!

Cut the dessert or cut the meal: worms remember differently

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Folk wisdom teaches us that old people tend to be more forgetful than they were in their prime. Sadly, folk wisdom also teaches us that everybody gets old eventually. In more scientific terms, declines in learning and memory capabilities are often seen alongside human aging. It seems reasonable, therefore, to postulate that molecular factors of longevity (some of which were previously found in C.elegans, among other organisms1) are likely to mediate cognitive health, e.g. memory formation, as well.

But how might one go about testing this postulate? Or more specifically, how might one test the effects of aging factors—and the potential treatments targeting such factors—on memory formation within the half-life of graduate students and postdocs? Moreover, a confounding observation in humans is the apparent reduction in cognitive capacity (e.g. short-term memory2) without detectable neuropathology/neurodegeneration in late life3. Therefore, to study aging without becoming terribly aged, and to track the progress of cognitive decline independently of pathological onsets, one ought to seek a model organism. This organism ought to:

  1. provide abundant genomic information,
  2. be a suitable subject for well-developed molecular tools,
  3. experience little neuronal turn-over or age-related neurodegeneration, and
  4. possess naturally short life spans (measured in days, not weeks).

Dr. Coleen Murphy and her colleagues at Princeton chose the tiny, bacteriophagic worm C.elegans (pictured below), which has all four aforementioned benefits1, 4, to investigate the (possibly therapeutic) potentials of known longevity-inducing mutations in memory formation.

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Elegant, truly.

Their 2010 study in PloS Biology, for example, examined two well-known C.elegans longevity regulators, Insulin/IGF-1 Signaling (IIS) and Dietary Restriction (DR).5  The primary molecular factor of IIS are the DAF-2 insulin receptor, which has a human homolog, and its target DAF16/FOXO (a transcription factor). As one might expect, daf-2 mutants with low insulin signaling maintain chemo/thermotaxis abilities better with age. Another gene, aptly named EAT-2, is involved in DR, whereby eat-2 mutants (whose feeding is impaired due to inefficient pharynx movement) live up to 50% longer than wild type.

IIS Insulin/IGF-1 Signaling (impairment leads to longevity) DAF-2 Insulin receptor involved in IIS. STAM Short-term associative memory
DR Dietary restriction(can be caused by eat-2 mutation) EAT-2 Nicotinic acetylcholine receptor involved in DR. LTAM Long-term associative memory
CREB Transcription factor implicated in memory formation Crh-1 CREB gene in C.elegans DAF16/FOXO Downstream transcription factor of DAF-2

A table of jargon.  Every paper should have one. 

While both IIS and DR have been shown to affect cognitive performance in mice6, 7, little is known about their effect on memory formation5. Murphy et al. designed, therefore, positive olfactory associative assays to measure learning and memory in aging wildtype, daf-2, and eat-2 worms. First, after starving the worms briefly, food was given alongside with butanone, which by itself is only a weak chemoattractant. After butanone-accompanied feeding, worms were then segregated into short-term associative memory (STAM) and long-term associative memory (LTAM) training groups, followed by learning efficiency (i.e. post-conditioning response toward butanone) and memory testing.

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Diagram for positive associative olfactory learning and memory assays. (Fig. 1A, Murphy et al.)

In all worms, STAM was demonstrated by elevated response toward butanone for about 2h post-training, and LTAM was observed as the post-assay retention of association between food and butanone after the 16-24h resting periods. LTAM, and not learning/STAM, was affected by disruptions of transcription/protein synthesis, and Murphy et al.’s genetic screening yielded the (nigh legendary) transcription factor CREB as requisite for LTAM. The CREB deletion mutant, crh-1, learned the association as fast as its peers, recalled the information well by worming its way toward butanone with alacrity, but forgot everything by 2h post-training and had to start anew. Murphy et al. posited, then, that learning is molecularly distinct from, but required for, LTAM. Longevity factors that affect learning and STAM, then, may act through pathways unrelated to LTAM, and vice versa.

fig3ab

Left: CREB mutant’s learning (peak of index at time 0) efficiency and loss of STAM are not significantly different from wildtype. Right: LTAM in CREB mutant is significantly reduced compared to wildtype, as tested by spaced training with 16h rest periods. (Murphy et al., Fig. 3A,B)

During the first week of C.elegans adulthood (ending at the equivalent of, one might imagine, somewhere between mid-life crisis and retirement), Murphy et al. found aging to be accompanied by learning, STAM, and LTAM declines in wildtype. In early adulthood, IIS-impaired daf-2 mutants (whom one’s youthful brain surely remembers from a previous paragraph) were shown to be better at forming STAM and LTAM, with the enhancement dependent on the downstream DAF-16/FOXO being unperturbed. Early adult eat-2 mutants, however, did not show such cognitive enhancement, and even had mild LTAM deficiency.

The two mutants, then, while having similar pro-longevity effects, act independently in maintaining cognitive function. Would this hold true for their effects—if any—on cognitive decline as well? Indeed, as already hinted with the CREB mutant, Murphy et al. observed LTAM impairment on par with wildtype in the aged daf-2 mutants, who still learned better than old wildtype worms did. Aged eat-2 mutants also learned better than their ordinary peers, and proved to be cognitively better preserved even than the daf-2 mutants, as eat-2 mutants had greater LTAM retention than wildtype, despite the slight handicap they suffered as young adults.

fig8bc copy

Top: loss of LTAM seen in aged wildtype and daf-2 mutant. Bottom: retention of LTAM seen in eat-2 mutant. (Murphy et al., Fig. 8B,C)

In sum, the requirement of CREB in C.elegans memory could count as an important discovery (although it did not come as a surprise), especially when it turned out that crh-1 gene could be useful as an estimator of LTAM (based on its LTAM-correlating expression level in aged daf-2 and eat-2 mutants). Another achievement, as suggested in this study’s title, was the support provided for the following conjecture: specific types of longevity treatments (such as IIS/DR) could have either positive or negative effects on learning and memory, depending on the time window in which one investigates, even when restricted to relatively well-known pathways in a simple model organism.

So, to speak more fantastically: would one prefer to undergo IIS (i.e. “cut the dessert”), gain the cognitive edge and prosper as an elite, then live a mental invalid’s dreary, forgetful old age? Or would one rather suffer DR (i.e. “cut the meal”), becoming a painfully average run-of-the-miller forever, so as to have a long and mentally active (averagely active, of course) retirement? Choose wisely.

As part of the UCSD Neurosciences Graduate Program Seminar Series, at 4:00pm on Tuesday, January 21, 2014, in the CNCB Large Conference Room, Dr. Coleen Murphy will give a talk on CREB regulation in C.elegans neurons of a long-term memory network. It is advisable to come early and avoid worming one’s way in.

Xi Jiang is a first year student in the UCSD Neurosciences Graduate Program. After a brief tenure as a jellyfish obstetrician, he is now a rotation student under the guidance of Dr. Eric Halgren, studying sleep spindles.

References:

  1. J.F. Morley, R.I. Morimoto. Regulation of longevity in Caenorhabditis elegans by heat shock factor and molecular chaperones. Molecular Biology of the Cell 15: 657-664 (2004)
  2. J.H. Morrison, P.R. Hof. Life and death of neurons in the aging brain. Science 278: 412–419 (1997)
  3. P.A. Boyle, R.S. Wilson, L. Yu, A.M. Barr, W.G. Honer, J.A. Schneider, D.A. Bennett. Much of late life cognitive decline is not due to common neurodegenerative pathologies. Annals of neurology 74: 478-489 (2013)
  4. L.A. Herndon, P.J. Schmeissner, J.M. Dudaronek, P.A. Brown, K.M. Listner, et al. Stochastic and genetic factors influence tissue-specific decline in ageing C.elegans. Nature 419: 808–814 (2002)
  5. A.L. Kauffman, J.M. Ashraf, M.R. Corces-Zimmerman, J.N. Landis, C.T. Murphy. Insulin signaling and dietary restriction differentially influence the decline of learning and memory with age. PLoS Biology 8: e1000372 (2010)
  6. M.A. Akanmu, N.L. Nwabudike, O.R. Ilesanmi. Analgesic, learning and memory and anxiolytic effects of insulin in mice. Behavioral Brain Research 196: 237–241 (2009)
  7. L.W. Means, J.L. Higgins, T.J. Fernandez. Mid-life onset of dietary restriction extends life and prolongs cognitive functioning. Physiology & Behavior 54 503-508 (1993)

Big Data in human neuroimaging

The next speaker in the UCSD Neuroscience Seminar Series (4pm on Tuesday, 1/7/2014 in the CNCB) will be Arthur Toga. His talked, titled “The Informatics of Brain Mapping”, will emphasize the importance of Big Data and the use of multi-site neuroimaging projects to characterize the structural and functional changes in the human brain from clinical disease. Dr. Toga leads the Laboratory of Neuroimaging (LONI), a large national resource for neuroimaging data, atlases, analysis routines, and collaborative work on clinical disease.

The Alzheimer’s Diseases Neuroimaging Initiative (ADNI) is one example of this Big Data approach to human clinical neuroscience. Fifty-seven different sites contribute data to the LONI repository, from cognitive tests to structural MRI scans to genetic and biochemical markers from over 800 subjects with mild cognitive impairment and Alzheimer’s disease (AD). These data have been used to predict progressive atrophy and the transition for mild impairment to AD. Both decreased hippocampal volume and a more expansive pattern of gray matter loss predicted transition to AD. Moreover, others in the ADNI group found that both the well-known APOE4 allele and the GRIN2b allele contributed to MRI atrophy (summarized in Toga 2012).

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The data flow from multiple data acquisition sites to the archive and processing core at the Laboratory for Neuroimaging led by Dr. Arthur Toga. From Toga 2012.

I visited LONI at UCLA last spring, before they moved to USC this past fall. The huge facility, complete with a supercomputer adorned with blinking LED lights, walls plastered with journal covers designed by the in-house graphics team, a software team devoted to data storage and pre-processing, embodies their big collaborative approach to understanding human clinical neuroscience. In addition to projects on Alzheimer’s, autism, and schizophrenia, the lab is building infrastructure and technology to handle such large datasets and to allow researchers from all over the world to access and analyze them (Van Horn & Toga, 2009). I’ll end with some beautiful examples of their visualization software.

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Color-coded white matter tracts (Randy Buckner & Bruce Rosen with the Visualization Group at LONI).

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A visualization of white matter growth in autistic and control children. Carlos Mena, LONI

References

Toga, A.W. 2012. The clinical value of large neuroimaging data sets in Alzheimer’s Disease. Neuroimaging Clin. N. Am. 22: 107-118.

Van Horn, J.D., Toga, A.W. 2009. Multi-site neuroimaging trials. Curr. Opin. Neurol. 22: 370-378.

Molecular routes of memory enhancement

Most students are no strangers to cognitive enhancers such as caffeine or Adderall. However, these and other cognitive enhancers tend to have non-specific effects on the nervous system (e.g., jitteriness), or are specifically formulated for a disease or disorder1. Drugs or treatments that specifically target some aspect of a cognitive behavior are lacking, and require a fine-grained understanding of the cellular and molecular mechanisms that underlie that behavior. Cristina Alberini, a professor at NYU, is studying how specific genes, proteins, and pathways that mediate the formation and retrieval of long-term memories. She and her colleagues discovered that administering a protein to rats soon after training strengthened their memories even three weeks after the initial training2. This could represent a new target for memory enhancing therapies by direct analogy: could a similar protein be administered to humans to produce similar effects? (And would this work as a treatment for memory loss in dementia or Alzheimer’s?) Moreover, those with an interest in molecular signaling pathways may find the proposed mechanism intriguing for its implications for long-term potentiation across synapses. More generally, their evidence also coheres with theories of distributed memory traces across the brain.

Read on for a brief introduction to Dr. Alberini’s research, and learn more by attending her talk on October 22nd at 4pm in the CNCB conference room as part of the UCSD Neuroscience Graduate Program Seminar Series.

The researchers specifically find that this protein has effects on memory consolidation, which is a critical period after memory formation that stabilizes a memory and moves it to long-term storage.In a 2011 report to Nature, Alberini and her colleagues provide evidence that insulin-like growth factor II (IGF-2) is required during memory consolidation after training rats in an inhibitory avoidance paradigm (example here). In addition, bilaterally injecting IGF-2 into the hippocampus immediately after training enhances the strength of long-term memories (see A and B below).

nature09667-f3.2

(A) Latency to enter the dark/shock chamber is increased with hippocampal IGF-2 administration, both at 24 hours after training and 7 days after training. (B) 3 weeks later, memory of the foot shock experience persists in rats injected with IGF-2, as compared to vehicle-injected controls. (C) This effect generalizes to an auditory fear conditioning task–see text for details. (D) Injecting IGF-2 into the amygdala does not enhance learning, unlike injection in the hippocampus. Graphics from Figure 3 of Chen et al. 2011.

Specific mechanisms for IGF-2 memory enhancement

In addition, they find that the memory enhancing effects of IGF-2 depend on GSK3ß (glycogen synthase kinase 3 ß). Together these proteins increase the expression of GluR1 AMPA receptor subunits in the synapse. This finding is contrary to their original hypothesis that IGF-2, as a downstream target of C/EBPß (CCAAT enhancer binding protein), would affect memory enhancement by altering cell-wide mechanisms of transcription. Based on their evidence, they instead theorize that IGF-2 mainly enhances memory consolidation by acting at the synaptic level. This is confirmed when they inject IGF-2 into hippocampal slices and induce long-term potentiation (LTP) with weak high-frequency stimulation. As one might expect, IGF-2 not only enhances memory but also strengthens LTP.

The amygdala vs. the hippocampus

Alberini and her colleagues also show that injecting IGF-2 into the amygdala has no effect on memory (see D in the figure above). Although the amygdala is involved in the fear response and is critical to the inhibitory avoidance paradigm, it is not involved in the specific mechanism of consolidation enhancement that they investigate in this paper. This adds to previous evidence that the amygdala and hippocampus play different roles in memory formation, consolidation, and retrieval.4

Vy Vo is a first-year Ph.D. student currently rotating in the Reynolds lab at the Salk Institute. She is interested in perception, cognition, and information coding in the brain.

References

1 Graff, J., Tsai, L. 2011. Cognitive enhancement: A molecular memory booster. Nature 469, 474-475.

2 Chen, D.Y. et al. 2011. A critical role for IGF-II in memory consolidation and enhancement. Nature 469, 491-497.

3 “Memory Consolidation”, Wikipedia: http://en.wikipedia.org/wiki/Memory_consolidation.

4 Milner, B., Squire, L.R., Kandel, E.R. 1998. Cognitive neuroscience and the study of memory. Neuron 20, 445-468. As cited in Chen et al. 2011.