Every time I get worked up about all these neurogenesis findings I think about two words that return me to a state of inner peace, calmness, and….mental turmoil that all of my experiments will have to be performed twice: Septal and Temporal. Neurogenesis aside, the septal and temporal ends of the hippocampus are connected to different brain structures that cause the septal hippocampus to be more involved in spatial processing/cognition and the temporal hippocampus to be more involved in regulating stress and emotion. Which has the potential to explain everything.

Two posters today did a great job of analyzing neurogenesis in these different parts of the hippocampus and relating these findings to function. First, Tanti et al. showed that while a chronic stress model of depression reduced neurogenesis along the entire septotemporal axis, the antidepressant fluoxetine (aka Prozac) rescues this deficit specifically in the temporal hippocampus. In contrast, environmental enrichment, which may be viewed as more of a spatial and cognitive stimulus, selectively (and massively!) increased neurogenesis in the septal hippocampus with no effect in the temporal hippocampus. A nice dissociation where different classes of stimuli (drugs that regulate emotion vs. knowledge about objects and environments) regulate plasticity in different parts of the hippocampus.

This was complemented by a thorough study by Lehman et al., who recently showed that new neurons aid in the recovery from psychosocial stress, they asked whether “depressed” mice that suffered social defeat showed regional differences in neurogenesis. The prediction would be that neurogenesis should be specifically reduced in the temporal hippocampus, since this is the region that regulates the stress and emotional responses. They too were curious about the effects of environmental enrichment, since they’ve previously found that enrichment can rescue mice from a depressed state, but only if neurogenesis was present. The story sounds complicated when I tell you that they did all these experiments in normal mice and mice that had their adrenal glands removed, and had low levels if stress hormones (glucocorticoids). But a surprisingly clear picture emerged:

Social defeat (getting beaten up by a big bully mouse and then having to constantly live next to him) increased glucocorticoids and led to anxiety/depressive behaviors. Furthermore, social defeat specifically reduced neurogenesis in the temporal (i.e. “emotional”) hippocampus. The culprit was glucocorticoids - by removing glucocorticoids both the “depression” and neurogenesis impairments could be reversed. In a complementary experiment, they found that environmental enrichment is also a stressor, but a good stressor. Environmental enrichment increased glucocorticoids yet its other effects were beneficial – the mice were less anxious, less depressed, and they had increased neurogenesis. And just as with social defeat, the effects of environmental enrichment were also dependent on glucocorticoids: when glucocorticoids were removed, environmental enrichment did not reduce anxiety/depression and it did not increase neurogenesis.

The take home message is that stress hormones have bad effects on behavior and neurogenesis in the context of social stress, but they have good effects on behavior and neurogenesis in the context of environmental enrichment. And while we don’t yet know if septal neurogenesis is more important for spatial/cognitive behaviors and temporal hippocampus for emotional regulation, both of these posters did a great job of convincing me that this is a direction we need to pursue if we are to understand the many functions of new neurons. They also made it clear that there are complex interactions between stress, neurogenesis and behavior. To the point that I can live (for a little bit) with not knowing exactly how these neurons are working, but knowing that these diverse functions are clearly possible.

Of course, much can be learned while pinned down at your own poster. When you work in one lab, or one institution, your thoughts about the brain tend to have a specific focus based on the ideas of the people around you. Of course, new papers come out that challenge those thoughts but, man, papers move slooooowly. Scientists have not done a stellar job of using the internet to quickly communicate ideas. However, once a year at SFN a whole bunch of people come to your poster and give you their thoughts on the brain. Sometimes their thoughts are only presented in the language of distorted eyebrows and raised inflections but this is way better than typical social interactions with strangers, which usually go something like “You study brain cells/memory? Dude, I sure could use some more of those/that!”

So, what did my visitors think?

I had several visitors who specifically came by because they knew about me through the blog and through Twitter. Thank you for stopping by! You often never know if your online thoughts are useful, but I was happy to hear that several of you have used the blog as a teaching tool and a way to keep up with the field. I wish I could have these interactions with my readers more often than once a year at SFN. Then again I’d probably never be able to keep up with the comments so…no I didn’t say that – get engaged! I also heard one person, who does in vivo electrophysiology on my favorite brain regions, tell me that they’d tweet about neuroscience but they have nothing interesting to add to the conversation! Bollocks! Do you know how many in vivo electrophysiologists are on Twitter? Like, one? And how many experts are reviewing the literature on Research Blogging? Your knowledge is valuable. I would follow you in an instant.

“At first there was agreement on the behavioral function of neurogenesis but now everything is going in different directions.” Yes! Adult neurogenesis is a great example of the more you learn the more confused you get! Things may have seemed congruent 5 years ago but that was when there was only half a dozen studies that had examined the problems that arise when new neurons are ablated. Since then people have gone on to study more types of behavior and, as is also the case with the hippocampus, new neurons have been found to contribute to more and more types of behaviors. This has also given us additional opportunities for failed replication, and therefore doubt and confusion. One visitor commented on the recent paper that found memory impairments only if you kill new neurons after learning and we agreed that killing new neurons before behavioral testing could allow for other neurons to compensate, and make it appear that these new neurons are not doing anything significant. Of course, we have shown that often (e.g. at “baseline”) new neurons may be dispensable but that when an animal is stressed they are critical. And so explanations are emerging as to why some observe a behavioral function for new neurons and others do not, it’s just that it seems to be unbearably slow or remarkably fast depending on your mood.

I learned that there’s someone out there studying neurogenesis as related to maternal behavior….IN SHEEP! And they find that these neurons mature very slowly, like, primate slow. I love it when we think we have things completely figured out and then the data goes a totally different direction when you throw wool into the equation. Like, if we put cute little wool sweaters on our mice, would that make new neurons mature slower? One of the next big questions. Testable. Do it.

For those that missed my poster, fear not, for I have submitted it to Nature Precedings and will notify you here when they’ve posted it (couple days). For those that came to my poster today, sorry, you didn’t have to come to my poster.

Jason

Figshare may not be the only option, and others will likely crop up in the near future, but it is a good example of a solution. We all know about the internet, right? A medium for immediately publishing anything you want? I’m a little baffled that this has taken so long.

For my part, in my spare time, I will be scouring my hard drive for findings that have been forgotten. The beauty of publishing in a place like Figshare is that it is easy. Most of these experiments have already been analyzed (I mean, they were graphed so that we could discuss what to do/not do with them, now it’s just a matter of uploading the already-presentable data). Also, there’s no obligation to discuss the data ad nauseum (you’ll see some data descriptions are pretty short) though some degree of explanation is required to make the data useful. Furthermore, should the story evolve, there’s no reason why it can’t be also published in a traditional journal format. Check out Nature Publishing Group’s policy – they’re ok with publishing data (even full manuscripts) on preprint servers prior to submitting to their journals.

Of course, some data may still not be “shareable” such as patient data or data that is closely related to an ongoing project. But interestingness should never be a factor in my mind, because you never know what will be valuable to others.

So, if you’re “just” a summer student who thinks your 2 months of work can’t possibly amount to hard, citable evidence, think again. Ask your boss first, but think again.

My goal in compiling this list was to assess the magnitude of adult neurogenesis in primates. It’s definitely more challenging than assessing the magnitude of neurogenesis in rodents, which we know much more about, and so I had put it off. At this point I haven’t reached a clear conclusion but, in quickly skimming these papers, the number of proliferating cells and/or new neurons averages thousand(s) of cells in the young adult primate hippocampus. The range is much much larger, and many studies cannot be easily compared due to variability in the methods, which is partly understandable since primates are scarce and are often used in multiple studies, thereby limiting the analyses that can be performed.

Download the list

Some studies showing different functions of septal and temporal hippocampus

• Some of the best reviews of the topic are by Bannerman et al from 2004 and 2011.
• A recent and free review article by Fanselow and Dong.
• Classic Moser papers showing spatial memory is more dependent on dorsal hippocampus and anxiety/fear behavior on ventral hippocampus
• A recent paper suggesting that spatial processing in the septal hippocampus meets the behavioral-control functions of the temporal hippocampus to enable rapid spatial learning

History of neurogenesis quantification. So, back in the day, before I even knew what a neuron was, and before it was well-established that there was functional differentiation along the hippocampal axis, people would pick a few sections from the dorsal hippocampus (it’s much more photogenic, gets all the glory), count new neurons, and make it a density measurement. Then the stereology police arrived (seriously, that’s what they’re called) and pointed out that changes in tissue volume or cell packing could change density measurements without there being any differences in numbers of cells. Stereological analyses also prevent any biases that might arise from creating arbitrary boundaries when examining only part of the hippocampus. And so people started doing stereological counts, which require a systematic quantification throughout the entire hippocampus. My guess is that this probably delayed the appreciation that neurogenesis could vary in magnitude and function along the hippocampal axis. Now that we know that stereology is pointless we can get back to business (this is a joke – please don’t arrest me).

Difficulty of quantifying subregions due to curvature of the hippocampus. One of the reasons the hippocampus is such a popular neurobiological model is its anatomy – the dentate gyrus, CA3 and CA1 subfields are all composed of tightly packed cells that are easy to identify. Thinking of the hippocampus along its long axis, one end projects to the septum and the other abuts the temporal lobe, hence “septotemporal” is technically the most accurate way to refer to the different ends of the hippocamus. The hippocampus is curved in such a way that you can actually cut it along any of the 3 spatial planes (X, Y, Z aka coronal, horizontal, sagittal) and hit the hippocampus perpendicular to the septotemporal axis somewhere, giving rise to the classic the trisynaptic circuit. However, because of this same curvature, sectioning the brain in only one of the three planes means that some portion of the hippocampus is not going to be cut perpendicular to the long axis, producing sections in which septotemporal coordinates are hard to define.

The 3D nature of the hippocampus using images from the Allen Brain Explorer:

Figure 1: The dentate gyrus subfield of the hippocampus (i.e. green banana), from its septal pole, extends caudally and laterally and then ventrally. Green axis=dorsoventral, red=rostrocaudal, yellow=mediolateral.

Others on the curvature problem:

So how can we divide the hippocampus? Many people work with coronal sections. Can we delineate boundaries between different hippocampal subregions in coronal sections? Banasr et al. has described a reproducible method for separating dorsal from ventral hippocampus using coronal sections. Here, the dorsal regions would contain a fair bit of mid-septotemporal hippocampus but indeed, only the dorsal sections would contain septal hippocampus and only ventral sections would contain temporal hippocampus:

Jayatissa et al. has horizontally sectioned the rat brain and then used anatomical coordinates to divide dorsal from ventral. This seems to be a good way to isolate pure, septal hippocampus but dorsal measures would again blur together the septal and mid-septal regions.

What if we wanted to separate the septal and temporal ends of the hippocampus? One method, described in Amaral & Witter, 1989 offers a solution:

I have used a similar approach (see here and here). One drawback is that you ruin much of the rest of the brain during the dissection process (insert but-who-cares-about-the-rest-of-the-brain joke here). Here’s a figure from my thesis that illustrates the similar-shaped hippocampal slices obtained with this method:

Another strategy, which I can currently exploring since I’m working with coronally-sectioned brains, isn’t too different from the method of Banasr, above. To get at the septal hippocampus I’m just being a bit more selective and only examining portions of the dorsal hippocampus that extend quite far rostrally. For the caudal sections that contain both dorsal and ventral hippocampus the rhinal fissure seems like a good guide – anything falling on the ventral side I’m counting as ventral.

But if you’re lazy…

A fast, revolutionary new method for examining the hippocampus along its full septotemporal axis in a single section! It almost sounds too good to be true. In fact, it is. But it provides some interesting pictures for those of you who have stuck with me this far.

Recently, we irradiated a lot of rats to eliminate adult neurogenesis. Before coming to any conclusions about the behavioral data we needed to know whether neurogenesis was completely blocked AND whether it was blocked throughout the entire dentate gyrus. We were too lazy to cut hundreds of sections for each rat so what we did was extract the hippocampus but instead of sectioning perpendicular to its septotemporal axis, we sectioned it parallel to, or along, its septotemporal axis by flattening and freezing it on a microtome stage. With this approach we could cut the entire dentate gyrus in about 30 sections and get sections that had the entire septotemporal length of the dentate gyrus present. We then stained them for NeuN and DCX to visualize neurons and immature neurons, respectively. I think every other section was stained; one example is shown below.

Is it really necessary to divide septotemporally? I guess it depends. Many studies that have focussed more on dorsal vs. ventral have made significant findings. If the anatomical method is well-described and reproducible, what more could you ask for? It’s possible, however, that combining different septotemporal regions into the same analysis could obscure a result. For example, when I examined activation of new neurons after water maze training I found a steadily-increasing amount of activation as I went from septal to temporal (see Figure 7). Had I pooled the 2 septal quartiles together and pooled the 2 temporal quartiles together the observed difference would have been much smaller than when comparing the septalmost quartile with the temporalmost quartile.

Figure 7: The density of ‘activated’ new neurons (i.e. PSA-NCAM+ and Fos+) increased from septal to temporal. Note the mid-septal and mid-temporal regions were similar. Also note that I used D and V nomenclature, for ‘dorsal’ and ‘ventral’, despite repeatedly emphasizing in this post that ’septal’ and ‘temporal’ is more accurate. A reviewer told me to do this (and I listened).

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and now…

Pretty pictures from these sections!

Thanks and credit to Sarah Ferrante for sectioning, staining and imaging the tissue.

]]> http://www.functionalneurogenesis.com/blog/2011/04/dorsoventral-vs-septotemporal-hippocampus/feed/ 4 http://www.functionalneurogenesis.com/blog/2011/02/how-does-the-brain-pick-which-neurons-to-use/?utm_source=rss&utm_medium=rss&utm_campaign=how-does-the-brain-pick-which-neurons-to-use http://www.functionalneurogenesis.com/blog/2011/02/how-does-the-brain-pick-which-neurons-to-use/#comments Wed, 16 Feb 2011 16:31:25 +0000 Jason Snyder http://www.functionalneurogenesis.com/blog/?p=1060 Wiring. That’s one answer to this question. We know this from topographic maps in the thalamus and neocortex, where the basic units of sensory information are neatly represented in spatially-arranged populations of neurons – the various body parts are represented in specific locations, as are the different frequencies of sound, the different parts of the retina, and different odors and tastes. This basic sensory information has to be represented (i.e. we all need a faithful representation of visual elements, we all need to hear the various frequencies of sound that make up human speech etc.) so why not hard-wire it and make its representation the same for all of us?

It’s often thought that things change as you move into parts of the brain that represent more complex and abstract concepts. For example, in the hippocampus, many neurons receive the same inputs so it’s generally assumed that different neurons are equally capable of representing a given piece of information. While wiring between neurons must play a role in determining which neurons are activated, the diffuseness of the wiring means that related information need not be stored in spatially neighboring neurons as in the sensory regions of neocortex. Indeed, if you look at hippocampal neurons activated by a given experience they don’t appear to have any particular spatial arrangement but are randomly distributed, anatomically. Alternatively, it could be that certain hippocampal neurons are hard-wired to respond to specific stimuli, it’s just that we don’t understand the wiring.

I’ve mentioned before (here and here) how anatomical patterns of activity in the hippocampus are not always so random – in the dentate gyrus the same neurons are often repeatedly activated and by very different experiences. Furthermore, half of the dentate gyrus (the infrapyramidal blade) never seems to be noticeably active, period. But anatomical biases have been reported outside of the dentate gyrus too. Hampson and Deadwyler showed that spatial and nonspatial information is segregated in distinct septotemporal regions of CA1/CA3. Also, Nakamura et al. have suggested that CA1 neurons that represent a given spatial environment are more likely to be spatially clustered together.

While these studies suggest there may be a hard-wired anatomical pattern by which information is represented in regions such as the hippocampus, we really have have no idea how that pattern might be established. I was therefore intrigued to see a couple papers shed new light on this issue. One is a recent paper by Yassin et al. who used a Fos-GFP mouse to identify and record from neurons recently activated by behavioral experience. Fos is an immediate-early gene that is upregulated in neurons that are involved in learning and so, in this mouse, those neurons fluoresced green and could be examined electrophysiologically. They found that the Fos-GFP neurons fired at higher rates than neighboring neurons that were not expressing GFP and that they tended to be more connected to one another (and thus they were dubbed Facebook neurons), suggesting that there may be a subset of neurons that is preselected to be involved in representing experiences (perhaps not unlike the population of highly-active dentate gyrus neurons). There is a bit of a chicken and egg problem here, because we don’t know if the GFP+ neurons always fire at higher rates (and are hard-wired to be more involved in representing experience) or if they only fire at higher rates because they were recently activated (i.e. behavior-induced plasticity changed them). Intriguing nonetheless and a good approach for future studies I think.

The other study is pretty revolutionary I think and also has to do with predetermined, hard-wired patterns of neuronal activity. One of the exciting developments of the last 15 years has been the finding that patterns of neuronal activity are replayed during sleep. It is thought that this “replay” is the physiological correlate of memory consolidation, i.e. the rehearsal of recent experience and integration of that new information into the brain’s circuitry. Now, Dragoi and Tonegawa have found that the patterns of neuronal activity, seen as a mouse explores a novel environment, can also be seen during rest/sleep episodes before the mouse has ever been in that environment. Essentially, they discovered that the brain has created a representation (or at least a fraction) of an experience that has not even happened yet. They call the phenomenon “preplay”.

The preplay phenomenon does fit with previous data. The Mosers, in their News and Views piece on this study, note that “…place cells continue to fire in regular sequences when an animal’s position is fixed, for example, when a rat is running in a wheel. Moreover, rat pups exploring an open space for the first time show adult-like place cell sequences, which indicates that path sequences are hard-wired in the synaptic connection matrix by either genetic programs or early experience.” Also relevant is the finding from John Guzowski’s lab showing that very brief experiences (perhaps too brief to be even remembered) are capable of inducing transcription of the plasticity-related gene, Arc, in a full complement of CA3 neurons. In contrast, CA1 neurons were only fully activated after multiple experiences over multiple days, suggesting less of a role for hard-wiring and more of a role for plasticity and learning in shaping neural representations in this region.

Why preplay? One interesting hypothesis is that the hippocampus is needed to imagine the future (a reasonable role for a structure responsible for remembering the past). Could preplay be an attempt to predict future experience? Or might a shared pattern of activity simply be a way to bind together two events and create a coherent history? Don’t worry – I’m sure that, as we speak, there are rodents with implanted electrode arrays running around, working hard, to give us the answer.

Yassin L, Benedetti BL, Jouhanneau JS, Wen JA, Poulet JF, & Barth AL (2010). An embedded subnetwork of highly active neurons in the neocortex. Neuron, 68 (6), 1043-50 PMID: 21172607
Dragoi G, & Tonegawa S (2011). Preplay of future place cell sequences by hippocampal cellular assemblies. Nature, 469 (7330), 397-401 PMID: 21179088

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I’m kidding, of course, about the predictions.  But I do think “pattern separation” is starting to interest a lot of people and that it would be worthwhile to step back and consider how we got here (and where we are).

In the past year or so there were two very interesting papers by the Bussey and Gage groups relating adult hippocampal neurogenesis to pattern separation.  One paper (Clelland et al., 2009) is notable because, in my opinion, it is one of the strongest papers to date linking adult hippocampal neurogenesis to a psychological process.  The paper rises above previous work because it uses two independent behavioral tasks to assess a single psychological construct.  Compare this to the usual single-task approach—for instance, the work demonstrating that blocking adult hippocampal neurogenesis impairs contextual fear conditioning (… to pick an example at random).  The fear conditioning finding, by itself, gives us relatively little insight into the underlying psychological mechanism: is it a failure to remember the shock context, a failure to associate a context with an aversive outcome, a failure to discriminate the shock contexts from other contexts?  One way to generate more specific mechanistic information is to use multiple tasks that vary in their behavioral requirements but share a common psychological requirement.  This is the approach used by Clelland et al., who demonstrated the blocking neurogenesis impairs the ability to discriminate nearby (but not far-apart) spatial locations. The paper used two tasks that differed substantially in their behavioral requirements: One task was an operant location discrimination in mice; the other was a spatial version of delayed-non-match-to-place in rats.  The fact that both tasks detected a similar effect of arresting neurogenesis constitutes pretty strong evidence that adult hippocampal neurogenesis plays a role in fine spatial discriminations.  (The other paper showed that spatial discrimination performance is enhanced by exercise, which potently stimulates neuronal proliferation in the hippocampus.)

So there you have it: the function of adult hippocampal neurogenesis is pattern separation.  But how much does this really tell us about the psychology? If you think about it, almost any psychological process constitutes a form of pattern separation.  Take a simple classical conditioning experiment: a tone is paired with food.  To learn the association, the subject must isolate the tone from among the blooming, buzzing confusion of other sensory stimuli present during the trial.  The pattern of sensory stimulation provided by the tone must be separated from the sound of the whirring exhaust fan, the odor of fresh bedding, the sight of the illuminated houselight, etc.  This is pattern separation.  Take another example: probabilistic category learning (e.g., the weather prediction task).  The subject learns to predict an outcome using an assortment of multidimensional stimuli, with each stimulus providing only partial information about the outcome.  With practice, the subject learns to separate the complex patterns of stimuli into different categories.  This is pattern separation.  Except the hippocampus (and, by inference, adult hippocampal neurogenesis) is not very important for either of these tasks.

The term “pattern separation,” as people are currently using it, comes from the computational literature, where it refers to an operation in which overlapping patterns of input stimulation are transformed into non- (or less-) overlapping patterns of output stimulation. Through pattern separation, stimuli that activate a common population of neurons are made to activate separate populations of neurons.  According to several models (e.g., O’Reilly & McClelland, 1994), this operation occurs in the dentate gyrus.

So how does this computational pattern separation map onto behavior?  Ray Kesner’s group has been investigating this question for a while, and they have shown that lesions of the dentate gyrus impair fine spatial discriminations but do not impair other types of discrimination, such as temporal order or reward magnitude discriminations.  Work by the Leutgebs and Mosers confirms that the dentate granule cells are very sensitive to small spatial changes (read more about this here). But dentate gyrus pattern separation is not strictly spatial.  A recent human imaging study (Bakker et al., 2008) showed that the dentate gyrus (and possibly CA3) are activated when people see objects that are very similar to –but not the same as– objects they’ve seen before.

More generally, we know that the dentate gyrus receives input from most or all sensory modalities, suggesting that the dentate gyrus does not operate solely on spatial information.  So, while it seems clear that adult hippocampal neurogenesis has an important role in spatial pattern separation, some key questions remain.  Is dentate gyrus involved in pattern separation across multiple perceptual modalities?  If so, what defines dentate gyrus pattern separation relative to the other types of pattern separation in the brain? And does adult hippocampal neurogenesis contribute to all types of dentate pattern separation or to only a subset?

For answers, stay tuned to the next 369,999,999 papers.

A fundamental property of the hippocampus is its ability to rapidly encode memories while simultaneously keeping them distinct. Recording from hippocampal neurons one can clearly see that different populations of neurons are active as a rat explores two environments. This is thought to be one mechanism by which information is kept distinct in the brain.

For the last 15-20 years it has been thought that the dentate gyrus (DG), a major subfield of the hippocampus, serves to take small changes in incoming sensory information and orthogonalize them (i.e. make them more different). This idea was built in part on the fact that there are many more DG neurons than upstream cortical neurons. Thus, the DG could use completely different populations of neurons to represent different sets of incoming information and then pass on these representations to CA3, which may bind them into coherent events/memories (the interconnectedness of CA3 neurons, via “recurrent collatorals”, is thought to be a mechanism by which the different components of a memory are bound together).

However, a “problem” arose when Leutgeb et al. found that it is always the same population of dentate granule neurons (~1% of the total population) that are active as an animal explores different environments, even very different ones. This was a bit of a surprise. Still consistent with the proposed role of the DG in orthogonalizing information, however, was the fact that the DG neurons fired (i.e. generated action potentials, which transmit information from neuron to neuron) at different rates/frequencies in the different environments. Thus, changes in sensory information were represented by changes in patterns of activity within the same population of cells, not by recruiting different populations of cells. This is but one study – the question of how the DG encodes and extracts information is far from settled (e.g. what are the other 99% of granule neurons doing? Surely there is a situation in which they are active, no?). But the findings were robust and raise many questions, namely: How does the same population of DG neurons activate different populations of downstream CA3 neurons, during different experiences?

Until now I had been in denial, fixated on trying to understand what types of behavioral experiences might activate different populations of dentate gyrus neurons. But maybe now it’s time to face the data.

The consensus, both in vitro (e.g. here and here) and in vivo (here), seems to be that if DG neurons are sufficiently active they can reliably activate CA3 neurons. Can a single population of DG neurons account for the amount of CA3 activity seen in the behaving animal? Well, 1% activation of the total DG population (1 million neurons) is 10 000 DG neurons. Each DG neuron contacts about 10 CA3 neurons. So if all active DG neurons activated all their downstream targets, then you’d expect about 100 000 active CA3 neurons – a third of the population. Indeed, about 20% of CA3 neurons are active when a rat explores a novel environment. So it’s possible. But it’s probably unlikely.

One reason it’s unlikely is that it doesn’t explain how different populations of CA3 neurons are activated by different experiences if it is the same population of DG neurons that are always driving them. In other words, since DG neurons are relatively hard-wired to CA3 neurons, how could a given DG neuron activate a CA3 neuron under some conditions and not others? One answer is that maybe it doesn’t – quite a while ago, McNaughton et al. showed that, even when the DG is lesioned, CA3 neurons are still able to selectively encode spatial locations as a rat traverses the environment, probably due to direct inputs from the cortex. And so perhaps the primary function of the DG is not to selectively activate different CA3 populations. However, the DG could certainly shape activity within CA3 or insert unique information into the CA3 network. How?

One possible mechanism, which may be dead obvious to electrophysiologists, is frequency itself. Leutgeb et al. found that frequency of activity is how DG neurons encode information and so frequency of activity may also be the way DG neurons transmit information to CA3 during different experiences.

It has been known for some time now that the output of DG neurons, the mossy fiber axons, show extraordinary frequency-dependent synaptic facilitation. Basically, as a DG neuron fires more action potentials over shorter periods of time, the amount of neurotransmitter it releases onto CA3 neurons increases (thereby increasing the likelihood a CA3 neuron will in turn fire action potentials and be recruited to participate in memory encoding). This means that at low firing rates, a DG neuron will activate some CA3 neurons and, at higher firing rates, it will recruit different or at least additional CA3 neurons.

Wouldn’t this cause a problem where, as DG firing rates increase, it is not different populations of CA3 neurons that become activated, but more populations? Well, it is known that some DG neurons increase their activity, and others decrease their activity, as an animal has different experiences, so the net activity in CA3 could remain constant, while still activating different CA3 populations. However, the DG-CA3 circuitry is certainly complicated enough to allow for other mechanisms. For example, while the dentate gyrus projects to CA3, and it is connections between these hippocampal regions that are thought to encode memories, DG neurons actually contact more inhibitory interneurons than CA3 neurons. Furthermore, there is a wide variety of synaptic connections between DG neurons and interneurons and these connections can be made weaker or stronger in a state- and frequency-dependent manner. Suffice it to say, by firing at different frequencies, it is plausible that a given DG neuron could activate different populations of interneurons, which in turn could inhibit different populations of downstream CA3 neurons, making them less likely to participate in memory encoding.

This ties in loosely to a peculiarity of the dentate gyrus that, until now, has just been a source of pretty histological images (to me) – the variability of calbindin expression in dentate gyrus neurons. Calbindin is a protein that binds calcium, it acts as a buffer, and gives DG neurons their property of facilitation (briefly: A single action potential in a DG neuron will travel down the axon and trigger the opening of calcium channels in the synaptic terminal at a CA3 neuron. Calcium is necessary for neurotransmitter release and subsequent activation of the CA3 neuron. Calbindin will bind this small amount of calcium, thereby preventing neurotransmitter release and CA3 activation. However, as the number and frequency of action potentials increases, calbindin will fail to effectively “mop up” the extra calcium and neurotransmission will proceed.). If you look at the picture at the top of this post, you can see that the amount of calbindin varies greatly in DG neurons. Immature DG neurons, identified by PSA-NCAM expression, are devoid of calbindin (arrows point to clear examples) and even when they are quite mature (10w of age) 40% will still be devoid of calbindin (see my data in this montage). Lastly, calbindin expression can be modified by experience. So the variable and modifiable expression of calbindin might be yet another mechanism by which DG neurons are capable of shaping activity in CA3 neurons. Indeed, at least one study, from Robert Sapolsky’s lab, has shown that genetically altering calbindin expression in the dentate gyrus dramatically influences DG-CA3 physiology and impairs memory.

Thanks to A.P. for posing the question.

Reference

Leutgeb JK, Leutgeb S, Moser MB, & Moser EI (2007). Pattern separation in the dentate gyrus and CA3 of the hippocampus. Science, 315 (5814), 961-6 PMID: 17303747

Below are what I hope to be comprehensive visual collages of all published timecourse experiments, where a certain property of new neurons is examined at multiple (≥ 3) different ages. They are grouped by studies of: 1) cell survival, 2) marker expression, 3) functionality, and 4) miscellaneous studies that do not quite fit into the first 3 categories. I’ve ordered the data roughly chronologically and have included the first author’s name and publication year so you can read deeper, if needed. Indeed, if you know these studies already, a brief look at the graphs will bring back the take home message. However, since the data is stripped of text, if the studies are unfamiliar, you’ll have to go to the original source to figure out what the heck they mean (use Pubmed to at least obtain abstracts for the original studies if I didn’t provide a direct link).

Personally, I like timecourse studies for the same reason I like to have all my music albums or books visible at the same time: at a single glance they provide a lot of information – each individual stage of maturation can be interpreted within a bigger picture. The result of these many hours of work will either be a) that the purpose of adult neurogenesis will become immediately clear, or b) that we’ll all have some fancy collages to pin on our bulletin boards and look intelligent.

The survival timecourse

New neurons are born and then many die. The survival timecourse answers the questions: How many new neurons are born? Where are they born and where do they end up, anatomically? How many of them survive and can their survival be altered? Survival timecourses are typically performed by injecting animals with a mitotic marker that will label new neurons as they’re being born, e.g. ³H-thymidine (old school), BrdU (tried and true – example), or a GFP-expressing retrovirus (new school). At a later date one can then detect these birthdated new neurons and count them, see where they’re located etc.

What do these survival timecourses tell us?

• many newborn neurons die between 1w and 4w of age but after that they all survive
  • neurons born during infancy are an exception as they DO die off many months after their birth (Dayer 2003), lending support to the sexy-but-underexplored idea that neuronal turnover might underlie memory turnover in the hippocampus
• the number of new cells labeled with a birthdating marker (e.g. BrdU) grows between 2 hours and several days after the birthdating marker is administered
  • this is caused by continued division of the stem cell or precursor cell that took up the marker in the first place (see expression timecourse, below). After a few cell divisions the marker gets diluted to undetectable levels.
• the general timecourse of cell death is similar in young and aged animals (McDonald 2005) and in mice and rats, although more cells die in mice (Snyder 2009)
• the addition and culling of newborn neurons in the monkey hippocampus (Gould 2001) follows a delayed timecourse compared to rodents
• CREB signalling is critical for neurons to survive between 5-7 days old (Jagasia 2009), NMDA receptors are critical for survival from 14-21 days (Tashiro 2006)
  • thus, CREB signalling would appear to regulate survival before new neurons have formed excitatory connections and are functional (see below) and NMDA receptors regulate survival during the early phase of excitatory synapse formation, when new neurons are just beginning to be able to contribute to behavior. Knowing how to regulate neuronal survival has obvious implications for disorders where reduced neurogenesis might be a causative factor.
• learning increases the survival of new neurons (Leuner 2004)
• learning does not increase the survival of new neurons (Snyder 2005)

The expression timecourse

All cell types within the body express different genes/proteins that serve the cell’s function. Since a muscle cell has a completely different function than a skin cell, it will naturally express different proteins. In like manner, a 1 week-old neuron is functionally distinct from a 4 week-old neuron and the two will also express different proteins (to some extent). Many people have taken advantage of this, using these different proteins as markers that identify a new cell as a neuron vs. a glial cell or, more specifically, an immature neuron vs. a mature neuron. By simultaneously visualizing (via immunohistochemistry) both the birthdating marker (e.g. BrdU) and these phenotypic markers, one can know both the exact age of the neuron and its general degree of maturity. For a 10 sec guide to cell labeling with BrdU and phenotypic markers, see here.

What do these expression timecourses tell us?

• some markers (proteins) are increasingly expressed as new neurons mature over 4 weeks (NSE, NeuN, calbindin)
• other markers are mainly expressed when new neurons are < 4 weeks-old (DCX, PSA-NCAM, calretinin)
• most studies have used the same markers (e.g. DCX, NeuN) to simply demonstrate that new cells are neurons, but some have examined expression of markers that are associated with a more specific function, such as glucocorticoid receptors (Cameron 1993, Garcia 2004) or vascular markers (Palmer 2000)
• BrdU (or other birthdating markers) labeled cells express cell division markers (e.g. Ki67) several days after BrdU is administered. This does not mean newborn neurons are dividing – what it represents is the continued division of the stem cell, or precursor cell, that was originally labelled. (therefore you can never know the exact age of a new cell, but pretty close)

The functional timecourse

The previous timecourses are all well and good but sheer numbers of cells say nothing about whether the new neurons actually work. And markers like DCX or NeuN may give a general hint at the maturity of a neuron but not much more. Functional timecourses address these gaps. A direct measure of function would be whether a new neuron has electrophysiological properties that enable it to process information (e.g. input and output synapses, action potentials). Less direct signs of function can be inferred from the morphology of a new neuron and whether a new neuron is capable of expressing activity-dependent immediate early genes.

What do these functional timecourses tell us?

• electrophysiology (Esposito 2005; Ge 2006), anatomy/morphology (Hastings 1999; Zhao 2006; Toni 2007; Toni 2008), and activity-dependent gene expression (Jessberger 2003; Snyder 2009) all point to new neurons forming their first synapses at 2-4 weeks of age
• around 4 weeks of age, new neurons go through a phase where they have enhanced synaptic plasticity (Ge et al 2007) and enhanced activation during behavior (Snyder 2009)
  • thus, at a young age, new neurons are more modifiable by experience than mature neurons. This may enable them to make a greater impact on behavior, either at this age or by shaping their further integration into the circuitry so they can alter brain function when fully mature and functional
• young neurons have distinct neurotransmitter profiles: initially they receive GABAergic inputs and later receive excitatory glutamatergic inputs (Esposito 2005). Notably, GABA depolarizes immature neurons (Ge 2006), unlike its typically-inhibitory effects on mature neurons. Also, immature neurons have a unique form of the NMDA receptor (NR2B), which endows them with their enhanced plasticity (Ge 2007).
• blocking CREB signalling (Jagasia 2009) or the depolarizing effects of GABA (Ge 2006) inhibits the functional maturation of new neurons
• by 8 weeks of age, new neurons are pretty much fully developed, though Zhao 2006, Toni 2007 and Toni 2008 show that 8-10 week-old neurons are still slightly underdeveloped, presynaptically and postsynaptically
  • does this mean that 8-10 week-old neurons, despite no longer having enhanced synaptic plasticity or enhanced activation during behavior, might still function differently than fully mature neurons?

Other timecourses

There are some timecourse-ish studies that, instead of examining new neurons of different ages, have examined the final fate of same-aged neurons that had been manipulated at different stages of their development. From the first 3 figures we can see that specific stages during a new neuron’s development are associated with enhanced plasticity, unique neurotransmitter profiles and increased likelihood of cell death. Therefore, it is very possible that the ultimate fate of an adult-born neuron depends on when experiences occur, relative to these different stages.

What do these timecourses tell us?

Several show us that experience can modify the number of new neurons, but that the magnitude and direction of the change depends on how old the neurons are when the animal undergoes the experience. For example:

• environmental enrichment enhances survival of new neurons mainly when the neurons are 1-2 weeks old (Tashiro 2007)
• spatial learning in the water maze enhances survival when the neurons are 5-10 days old (Epp 2007) or 1-2 weeks old (Kee 2007)
• the stress hormone corticosterone decreases new neuron survival when administered for 18-day, but not 9-day, stints during new neuron development (Wong 2006)
• Dobrossy 2003 and Olariu 2005 show the interesting but difficult-to-interpret findings that neuron addition can be increased or decreased depending on the extent of learning and the age of the neuron relative to the learning experience
• The Kee 2007 data suggests that fully mature, 10-week-old neurons are activated during memory retrieval only if they were old enough, at the time of  learning, to be involved in forming the original memory

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Hopefully it’s now clear that a 3 day-old neuron differs from a 3-week-old neuron from a 3-month-old neuron. We have seen examples where the developmental stage of a new neuron influences it’s functional maturation and survival, clues that could someday be used to manipulate adult neurogenesis for therapeutic purposes.

Lastly, comparing many of these timecourses side by side, I’m reminded why I like them so much: I trust them. By examining the same thing at multiple time points, each study inherently has a lot of controls. If you take a single time point out of some of these studies, say the % of new neurons that have excitatory glutamatergic inputs at 14 days-old in Esposito 2005 and Ge 2006, you might wonder, who’s right here? One finds 5%, the other finds 70%. I am curious as to why they differ but I still trust both of these studies and refer to them often because, within their respective timecourses, both 14-day data points make sense and, across studies, the 2 timecourses themselves do generally agree, even if they are slightly shifted.