Their question was simple: at what rate do newborn neurons mature in nonhuman primates? Their methods were also simple and easy to compare to previous studies in rodents: they used BrdU to label newborn cells and then they colabeled the BrdU+ cells with immature (DCX) and mature (NeuN) neuronal markers at different cell ages: 2 days, 2 weeks, 6 weeks, 11 weeks and 23 weeks.

First, they found that after labeling with BrdU the number of BrdU+ cells increased over the next 6 weeks. This fits well with the data from Gould and suggests that precursor cells in primates may divide much more infrequently, taking up the BrdU label at injection, retaining it for several days or weeks and then giving rise to additional BrdU+ cells upon redivision, etc etc until the BrdU is diluted.

Second, the BrdU+ cells they observed appeared immature for much longer than is typically seen in rodents. Whereas in rodents ~90% of new cells express the immature neuronal marker DCX within a couple days of birth, peak DCX expression wasn’t observed until cells were 6 weeks old. By 23 weeks, ~40% of cells still expressed DCX (contrast with rodents, where DCX expression is gone by ~4 weeks) and only 40% expressed the neuronal marker NeuN, which may be because the cells were not fully mature. Interestingly, the lone human BrdU study reported similar proportions of cells expressing NeuN (22%), perhaps also reflecting extended maturation. In addition to marker expression, dendritic branching was very rudimentary in DCX+ neurons. Six week old cells typically only had a single dendritic branch. By 11 and 23 weeks, cells increasingly had higher order dendritic branch patterns.

This study is significant because it suggests that to truly understand the function of adult neurogenesis in primates (perhaps including humans), we have to adjust our understanding of the timing of the whole process. One example that is provided by the authors is the temporal link between antidepressant action and neuronal maturation. The fact that antidepressants take several weeks to become effective in humans roughly matches the timecourse of maturation of newborn neurons in rodents and this is often cited as evidence that neurogenesis contributes to the antidepressant response (i.e. antidepressants increase proliferation of new cells and after several weeks those cells are functional and can counteract depressive behavior).  The authors may be right in a sense – it does suggest that the maturation process in primates may be too slow for increased proliferation to translate into functional neurons and improved behavior. However, it remains possible that antidepressants work by accelerating the maturation of older, immature neurons, which could still fit with this new prolonged timecourse. Regardless, when studying primates, we may have to wait longer after labeling new cells before we quantify them, to know how many mature neurons are added. We may have to wait longer to assess how experience affects synaptic or dendritic growth. We may have to wait longer after ablating neurogenesis before assessing behavior. We may have to wait longer before the next set of experiments can be completed and published…

Reference
Kohler SJ, Williams NI, Stanton GB, Cameron JL, & Greenough WT (2011). Maturation time of new granule cells in the dentate gyrus of adult macaque monkeys exceeds six months. Proceedings of the National Academy of Sciences of the United States of America PMID: 21646517

“Random” roundup because any posts linking to articles or ideas I’ve recently found noteworthy will never occur on a regular basis (as others manage to do – I applaud you) but only when enough interesting material has accrued and I have a spare moment. The links will, however, not be random. For example, you can expect many links to point to articles on adult neurogenesis or hippocampal function but will likely find few links directing you to photos of puppy dogs.

Dopaminergic Modulation of Cortical Inputs during Maturation of Adult-Born Dentate Granule Cells. A pretty thorough examination of dopaminergic modulation of synaptic transmission and synaptic plasticity in the dentate gyrus. Dopamine reduced synaptic transmission in both immature and mature granule neurons, but through different receptor subtypes. Additionally, dopamine reduced long-term plasticity in immature neurons but not mature neurons. Given the suggestion that dopamine could gate the entry of information into long-term memory, these findings suggest young and old neurons could have quite different behavioral functions.

Mu Y, Zhao C, & Gage FH (2011). Dopaminergic Modulation of Cortical Inputs during Maturation of Adult-Born Dentate Granule Cells. The Journal of neuroscience : the official journal of the Society for Neuroscience, 31 (11), 4113-23 PMID: 21411652

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Lidocaine attenuates anisomycin-induced amnesia and release of norepinephrine in the amygdala. Memory consolidation is the phenomenon by which memories are encoded through enduring structural changes in the brain and is often demonstrated by showing that memory loss occurs when you inhibit protein synthesis around the time of learning. This paper shows that one of the most commonly-used protein synthesis inhibitors, anisomycin, leads to increased norepinephrine release in the amygdala which could, by itself, impair memory.  The interesting final experiment showed that the effects of anisomycin on memory and norepiniphrine were reduced when the amygdala was totally shut down with lidocaine.

Sadowski RN, Canal CE, & Gold PE (2011). Lidocaine attenuates anisomycin-induced amnesia and release of norepinephrine in the amygdala. Neurobiology of learning and memory PMID: 21453778

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Evidence for the Re-Enactment of a Recently Learned Behavior during Sleepwalking. I’ve written a number of times about how neuronal firing patterns observed during waking experience are replayed during sleep, and could therefore reflect consolidation of memory and even dream content. Of course no one knows what rats are experiencing during sleep or whether they dream like us. To get around this problem, these authors trained sleepwalkers on a motor task with very defined arm movements and then examined sleepwalking behavior on the following night. Indeed, a video shows one subject who wakes up the following night and, for a few seconds, seems to be performing the same stereotyped task movements. Only one subject but tantalizing evidence and a cool experimental strategy nonetheless.

Oudiette D, Constantinescu I, Leclair-Visonneau L, Vidailhet M, Schwartz S, & Arnulf I (2011). Evidence for the Re-Enactment of a Recently Learned Behavior during Sleepwalking. PloS one, 6 (3) PMID: 21445313

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Increasing adult hippocampal neurogenesis is sufficient to improve pattern separation. One of the biggest questions in the neurogenesis field is whether adult-born neurons are important for behavior. Usually this is tested by examining behavior in animals that lack adult neurogenesis but many studies have correlated increased neurogenesis in enriched or athletic animals with “improved” behavior (smarter, less depressed etc). Of course, the major confound is that enrichment and exercise do many other things besides increasing neurogenesis. To get around this Sahay et al. made a mouse in which neurogenesis could be specifically increased in adulthood. These mice were better at discriminating between related contexts and, after exercise, showed much greater exploratory activity in an open field.

Sahay A, Scobie KN, Hill AS, O’Carroll CM, Kheirbek MA, Burghardt NS, Fenton AA, Dranovsky A, & Hen R (2011). Increasing adult hippocampal neurogenesis is sufficient to improve pattern separation. Nature PMID: 21460835

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Necessity of Hippocampal Neurogenesis for the Therapeutic Action of Antidepressants in Adult Nonhuman Primates. This study potentially bridges a big big gap by extending the role of adult neurogenesis in the antidepressant response from rodents all the way to monkeys. Chronic stress induced anhedonic and subordinate behaviors and these effects could be reversed with fluoxetine, but not in irradiated monkeys that had reduced neurogenesis. Could someone follow this up with a transgenic model?

Perera, T., Dwork, A., Keegan, K., Thirumangalakudi, L., Lipira, C., Joyce, N., Lange, C., Higley, J., Rosoklija, G., Hen, R., Sackeim, H., & Coplan, J. (2011). Necessity of Hippocampal Neurogenesis for the Therapeutic Action of Antidepressants in Adult Nonhuman Primates PLoS ONE, 6 (4) DOI: 10.1371/journal.pone.0017600

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Systemic 5-bromo-2-deoxyuridine induces conditioned flavor aversion and c-Fos in the visceral neuraxis. OH NOOO! Rats don’t like BrdU! These authors show that pairing a BrdU injection with exposure to a sweet palatable drink causes rats to avoid that drink in the future. It also leads to a mildly elevated stress response and elevated c-fos expression in areas of the brain that represent viscera, consistent with the possibility that BrdU could be exerting unpleasant effects in the gut, where there is a lot of cell division. The authors conclude that the effects on behavior in subsequent days and weeks are probably minimal (phew!), but I’d certainly keep these data in mind when considering injecting BrdU around the time of behavioral testing.

Kimbrough A, Kwon B, Eckel LA, & Houpt TA (2011). Systemic 5-bromo-2-deoxyuridine induces conditioned flavor aversion and c-Fos in the visceral neuraxis. Learning & memory (Cold Spring Harbor, N.Y.), 18 (5), 292-5 PMID: 21498563

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Compensatory network changes in the dentate gyrus restore long-term potentiation following ablation of neurogenesis in young-adult mice. In an interesting study of plasticity following neurogenesis reduction, these authors find that LTP was dramatically reduced after arresting neurogenesis, but only transiently. LTP recovered within weeks, possibly because of compensatory reductions in inhibition and enhanced survival of neurons born before neurogenesis ablation. Hat tip to Sil for this one.

Singer BH, Gamelli AE, Fuller CL, Temme SJ, Parent JM, & Murphy GG (2011). Compensatory network changes in the dentate gyrus restore long-term potentiation following ablation of neurogenesis in young-adult mice. Proceedings of the National Academy of Sciences of the United States of America, 108 (13), 5437-42 PMID: 21402918

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That’s it.

1. Excitable: the ability to fire action potentials.
2. More excitable: fires action potentials, but more.
3. More LTP: not the same as more excitable.
4. Less inhibition: also not the same as more excitable, though the two may go hand in hand.
5. The Scholarpedia page on neuronal excitability, which was last modified on 13 August 2009, has been accessed 49,025 times, and contains no information (peer review is slow).

One of the claims that is often made is that adult-born neurons are more plastic and more excitable than older neurons.  This despite there being little evidence (until recently) that new neurons indeed are more excitable. But, hey, “excitable” sounds great alongside “plastic”. The Schmidt-Hieber paper did show that new neurons are more excitable, though it wasn’t their main focus and it is only occasionally referenced as evidence for greater excitability.

My misunderstanding that there are no thorough investigations of new neuron excitability was brought to an end recently when I was fortunate to have an infant-free moment (In which I was able to read two papers in the same evening, plus an entire New Yorker article over breakfast. Amazing.) One of the papers was Reliable Activation of Immature Neurons in the Adult Hippocampus by Mongiat et al. from Alejandro Schinder’s lab, which I really should have read long ago.

In the introduction the authors note that for new neurons to have a function, they must be able to generate action potentials (aka fire action potentials, aka spike), the basic unit of activity and communication between neurons. The degree of excitability of a neuron – it’s ability to fire action potentials – is influenced by its shape, ion channels and the number of synapses that are stimulating it. Coincident activation of distinct inputs onto the same neuron can boost the chances of action potential firing and lead to the integration of different pieces of information. And the same neuron can represent different information depending on the frequency of its firing. So, a good characterization of excitability will be necessary to understand how adult-born neurons process information.

To do this the authors recorded from individual GFP+ granule neurons of various ages. The key findings:

1. Smaller current injections were able to elicit comparable spiking in 24-29 day old neurons and mature neurons (see figure). 19 day old neurons were not very good at firing action potentials.
2. Synaptic stimulation evoked action potentials similarly in young and mature neurons. What is significant though is that young neurons had fewer synapses, yet were equally capable of responding to a given stimulus. The amount of synaptic current required to depolarize young neurons, and bring them closer to firing an action potential, was about half that of mature neurons.
   • young neuronal spiking was delayed and variable in response to synaptic stimulation, consistent with the possibility that their spikes could code information differently than mature neurons
3. Relative to mature neurons, young neurons had almost normal voltage-gated Na+ and K+ currents, which are the primary currents involved in action potential generation. In contrast, they had much weaker inward-rectifying K+ currents (Kir), which are generated by K+ channels that are always open and therefore always steer the cell towards a hyperpolarized state where it will not fire action potentials. As the authors put it, there is an “ongoing homeostatic regulation that maintains a high degree of excitability in immature DGCs by boosting currents involved in spike generation while limiting the development of Kir currents.”
   • When these Kir channels were blocked pharmacologically, mature neurons became like excitable young neurons. Furthermore, overexpressing Kir channels in young neurons made them less excitable, like mature neurons.

The take-home message to me seems to be that 3 to 4-week-old neurons, despite being rather immature, are fully capable of firing action potentials. They aren’t necessarily more active, but they are more excitable because they are more efficient at converting synaptic stimulation into firing behavior. Importantly, GABAergic inhibition was blocked for all of these experiments. Given that immature neurons have reduced inhibition they may be more likely to fire action potentials in the behaving animal or inhibited electrophysiology preparation. The other take-home message is that this group is great at consistently addressing physiological questions that are necessary for generating theories about how new neurons might contribute to circuit-level function and behavior.

Mongiat LA, Espósito MS, Lombardi G, & Schinder AF (2009). Reliable activation of immature neurons in the adult hippocampus. PloS one, 4 (4) PMID: 19399173

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

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.

1. A group of scientists reduce neurogenesis and report a memory deficit.
2. A second group repeats the experiment, with only a few minor differences in protocol, and fails to find a memory deficit.
3. A third group, using the same species as the first group but a protocol more similar to the second group, replicates the original finding but only when the experiment is performed on Wednesdays.
4. Faith is restored.
5. Five groups report no such neurogenesis-dependent memory deficit.
6. It is reported that developmental exposure to strontium reduces adult neurogenesis by 40% AND produces the much sought after memory deficit. In a technical tour de force follow-up experiment, artisanal cheeses restore neurogenesis and reverse the memory deficits. Causation is established.
7. BDNF.
8. Everyone proclaims the role of neurogenesis in memory and is totally confused at the same time.
9. Someone systematically examines all of the variables in the memory test to determine whether or not the whole thing is a hoax and they should just change careers**.
10. We have never gotten this far.

Even at level 8, the neurogenesis-fear conditioning story was one of the more convincing arguments of new neuron functionality. With this study by Drew et al. we may soon be jumping for joy as we appear to be graduating to level 9.

The contribution of adult neurogenesis to contextual fear conditioning was greatest when mice were only given a brief training experience – mice lacking adult neurogenesis showed reduced fear of a context where they previously received a single footshock during a brief (3 min) exploration session. With longer exposures to the context, or additional footshocks, neurogenesis-deficient mice showed normal memory. This finding could be explained by the fact that young neurons have a lower threshold for synaptic plasticity, allowing them to encode fleeting experiences that would be forgotten if left to mature neurons.

So, brief training protocols may now likely be my first choice, at least when using mice. In fact, the only times I have observed contextual fear memory deficits in mice has been after brief training protocols almost identical to those used by Drew et al. So we just might have taken a big step forward. If not, check back in 5 years for my revised “How progress is made” list.

*or any other field for that matter

**this is not entirely a joke because, in this case, it both 1) appears to not be a hoax, and 2) marks the launch of the next phase of Michael Drew’s career (congrats)

Reference
Drew MR, Denny CA, & Hen R (2010). Arrest of adult hippocampal neurogenesis in mice impairs single- but not multiple-trial contextual fear conditioning. Behavioral neuroscience, 124 (4), 446-54 PMID: 20695644

The main experiment is illustrated on the left. Groups of rats were either exposed to 4 different contexts (A,B,C,D) or a single context (D) over the course of several months (TRAINING EXPOSURE). Then rats were either exposed to all 4 contexts or the single context over the course of half an hour (TEST) and expression of the immediate-early gene, Arc, was used to identify “activated” neurons. The idea is that, if subsets of granule neurons encode memories at different times in an animal’s life, then recalling those distant memories at test may re-activate those granule neurons. Alme et al. provide two hypotheses that their experiment will test and I illustrate them below with black-eyed peas.

1) The selective tuning hypothesis.

According to the selective tuning hypothesis, different populations of newborn granule neurons (i.e. those in their critical period) encode the contexts explored at each point in an animal’s life. As these cells mature they become less active and no longer encode new information, causing subsequent experiences to be encoded in newly-added populations of granule neurons (top half of above figure; red cells for A, blue cells for B, green cells for C*). However, even when old and unexcitable, granule neurons that were originally involved in encoding an experience (back when they were immature), are reactivated during recall of that experience. In other words, the neurons are selectively tuned to memories for events that happened at a specific point in time. Thus, during the test phase, when group #1 rats are exposed to contexts A, B, C, and D, a relatively large proportion of granule neurons will be activated.

In contrast, group #3 (bottom half) will also experience all 4 contexts during the test but would be expected to have fewer activated neurons because the contexts are novel and will therefore all be encoded, for the first time, in the same cohort of granule neurons (those that are in their critical period at the time of testing – shown as orange cells).

2) The retirement hypothesis.

Under the retirement hypothesis, again, only immature neurons are involved in memory encoding. However, unlike the selective tuning hypothesis, as neurons mature they become unexcitable to the point that they are never reactivated, even when recalling the memories that were formed during their critical period (top half of figure). So, during the testing phase when memories for A, B, C, and D are recalled, it is only the currently-immature population of neurons that is activated (the orange cells) – a much smaller population of granule neurons is activated compared to the selective tuning hypothesis. Additionally, one would expect that testing in A, B, C, D would activate the same number of neurons regardless of whether or not a rat had previously experienced the contexts (compare top and bottom halves of figure) because, at the time of test, neurons that may have previously been involved in encoding these contexts are no longer functional and it is only the currently-immature population of neurons that is activated.

What did they find?

The data generally aligned with the predictions of the retirement hypothesis: the same number of cells were activated regardless of whether rats experienced ABCD for the first time (group #3) or were re-exploring familiar contexts (group #1). So, it would appear that recalling distant memories does not reactivate additional subsets of granule neurons that would have been involved in the original encoding of those experiences. So, the selective tuning hypothesis is out.

Exploring four contexts at test activated twice as many neurons as exploration of only a single context. So, different experiences are capable of activating different populations of granule neurons, but to a much smaller extent than in other regions of the hippocampus (CA1, CA3). Alme et al. do a bunch of math to show that the probability that a granule neuron is activated in multiple contexts is about 40 times more than would be expected by chance, if random granule neurons were activated during different experiences.

What does this mean for my dentate gyrus?

Does this mean that these silent granule neurons were once active, but have succumbed to old age? Perhaps, but it hinges on one assumption that is not yet settled – that young granule neurons are preferentially involved in storing/processing information. Since no one has ever directly compared activation of young, adult-born neurons with activation of mature, perinatal-born neurons (with a birthdating marker such as BrdU), it remains possible that many of the activated cells in this study are perinatal-born, mature granule neurons (which Alme et al. acknowledge).

Here are some of the studies, and their limitations, that address the issue of whether young, adult-born neurons are more “activatable” than mature neurons:

1. Many electrophysiological studies have shown that immature neurons have enhanced plasticity and excitability. But does this mean they’re more likely to be active during behavior? The young neurons that are more likely to undergo LTP also have fewer, maybe weaker, synapses than mature neurons. So, maybe they’re even harder to activate. I don’t know.
2. Tashiro 2007 nicely demonstrated that environmental enrichment increased neurogenesis and the total number of young activated neurons. However, the proportion of young neurons that were activated was not different from the general population.
3. Studies that compare activation of BrdU+ cells to activation of the general population (e.g. cells stained for DAPI or NeuN) could introduce sampling biases due to the different methods used to quantify immature neurons and other/mature neurons. For example, Kee 2007 found preferential activation of BrdU+ adult-born neurons relative to NeuN+ cells but newer work from the same group, comparing CldU+ perinatal-born cells with IdU+ adult-born cells found no differences.
4. A similar sampling bias could explain the enhanced activation of young BrdU+ cells relative to the general population in the study by Ramirez-Amaya 2006. Then again, Ramirez-Amaya examined rats and my own (indirect) data from rats also suggests adult-born neurons may indeed be more “activatable”.

In short, the verdict is still out on what would seem like a simple question to answer – does behavioral stimulation preferentially activate young neurons relative to mature neurons? A definitive answer to this question will shed a lot of light on the function of adult neurogenesis and the dentate gyrus. In either case, thanks to Alme et al. for being the first to address the potential role granule neurons might play in encoding time, even if the only definitive conclusion is that most granule neurons appear to be doing absolutely nothing. Clearly there is a long way to go before we understand exactly what the dentate gyrus is doing.

*I only had so many beans and so many colored markers, so I apologize for not illustrating context D, but I think you get the idea.

Reference

Alme, C., Buzzetti, R., Marrone, D., Leutgeb, J., Chawla, M., Schaner, M., Bohanick, J., Khoboko, T., Leutgeb, S., Moser, E., Moser, M., McNaughton, B., & Barnes, C. (2010). Hippocampal granule cells opt for early retirement Hippocampus DOI: 10.1002/hipo.20810

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

Neurons are not born with dendrites and spines – they are acquired during a developmental process that takes many weeks (see here & here). During early development, the pattern of formation of dendrites and spines are sculpted by experience, as might be expected if dendrites and spines are anatomical structures involved in processing and storing sensory information. While a body of work has emerged suggesting adult-born neurons are involved in memory and behavior, no one has yet investigated whether experience is capable of altering the dendritic development of these new neurons. This paper by Tronel et al. is therefore very important because it is the first to look at this phenomenon. They show a dramatic acceleration of dendritic development in response to learning, suggesting a potentially powerful role for new neurons in storing and processing information.

It has been 10+ years since Gould et al. and Kempermann et al. showed that learning and enriched environments can enhance the survival of new neurons. These findings are logical precursors to the current study since, if these new neurons have all the necessary components,  they suggest experience could add to the mnemonic functions of the hippocampus. But subsequent studies indicated that experience could also decrease the survival of new neurons. So perhaps structural changes to new neurons that are more relevant to learning might be worth investigating. For example, in many of my own experiments, I have failed to observe learning-induced changes in the number of new neurons but, if the number of dendrites or spines is increased, then there could still be an enhanced ability to process information. Or there could be the removal of some spines and the formation of others, suggesting a transformation in the type of information processed by new neurons. To get at these possibilities, Tronel et al. used doublecortin (DCX) staining and retroviral-GFP labeling to visualize the dendritic structure of newborn neurons in rats that had either remained in their cage (non-learners) or had learned a spatial memory task, the Morris water maze.

Since the authors had previously shown that water maze learning enhances the survival of 1-week-old cells, they first examined whether water maze learning would also alter the dendritic structure of this same population of neurons. Training rats for 6 days and examining new BrdU+/DCX+ neurons the following day (i.e. when new neurons were 14-days-old) they found that the dendritic length and the number of dendritic branches was doubled compared to rats that sat in their home cage.

More remarkable is the duration that the increased dendritic complexity persisted. To get at this question a GFP retrovirus was used to label new neurons born 1 week before learning, since DCX is eventually downregulated and cannot be used to examine dendritic morphology in neurons more than ~2 weeks old.  They found that even 3 months after learning, maze-trained rats had longer dendrites, more branch points, and more dendritic ends. The differences were not trivial either – maze-trained rats had ~70% increases for all of these measures. The number of spines (and therefore putative synapses) was also elevated, 3-fold, and the proportion of spines that showed a mature, mushroom-shaped morphology was 6-fold greater than naive, untrained rats. Since the dendritic morphology of developmentally-born hippocampal neurons can be altered by learning, physiological changes in hormones, and exercise, it is also worth noting that in this study learning did not affect the dendritic complexity of mature granule neurons (though spines were not analyzed in mature neurons and it is possible that learning caused retraction and formation of spines in mature neurons with no overall effect in spine numbers or morphology), suggesting adult-born neurons are particularly sensitive to learning-related activity.

They go on to show that these structural changes in adult-born neurons are even more pronounced when rats learn a more challenging version of the water maze task, where the spatial location of the escape platform moves on a daily basis. They also show these effects require NMDA receptors, which are required for many forms of hippocampal-dependent memory. These additional experiments are notable but it is the basic finding – the magnitude and duration of the structural changes – that is most interesting to me. Here are some of the reasons why:

• previous studies have suggested that adult-born neurons reach a plateau in their functional development by ~8+ weeks of age. These data suggest that new neurons still have a long way to go before they become fully mature.
• the 8w developmental plateau in earlier studies could be normal for animals that have not had any significant life experience (what does this mean when the majority of scientific studies of the brain use naive, deprived animals as models?)
• when experience accelerates the dendritic development of new neurons, are those neurons now less plastic and less likely to contribute to future behaviors? In trying to understand why some studies report behavior deficits after neurogenesis ablation whereas others do not, I’m imagining that 6 weeks of neurogenesis ablation could have major effects on behavior if older (>6w) adult-born neurons are less plastic, perhaps because experience (experimenter handling, group housing, previous learning) accelerated maturation in the way Tronel et al. report. In contrast, if animals have been deprived of learning experiences, 6 weeks of neurogenesis ablation might not have any effects on behavior, because older neurons are still relatively immature and able to compensate.
• depending on how you look at it, it is valid to wonder how a relatively small population of new neurons can be important for behavior. If you now consider the fact that 3-month-old cells still have significant amounts of untapped storage capacity, the cumulative numbers of new neurons generated over 3 months no longer seems so small and insignificant

Reference

Tronel S, Fabre A, Charrier V, Oliet SH, Gage FH, & Abrous DN (2010). Spatial learning sculpts the dendritic arbor of adult-born hippocampal neurons. Proceedings of the National Academy of Sciences of the United States of America, 107 (17), 7963-8 PMID: 20375283

Since the original Eriksson study, a number of studies have attempted to characterize adult neurogenesis in the human hippocampus, by immunostaining for endogenous markers of proliferating precursors and immature neurons, thereby getting around the inconvenient fact that most human brains do not contain BrdU (similar techniques have been used to characterize neurogenesis in wild animals). The problem is that the histology in human studies often looks markedly different than in rodent studies – antibodies don’t recognize human antigens the same as in rodents, human brains may not be preserved as well as in controlled animal studies, and human brain tissue is obtained when an unhealthy person dies. Thus, it can be very difficult to interpret human data based on what is known about adult neurogenesis in rodents.

DCX-expressing cells in a 65 year-old hippocampus

However, the recent study by Knoth et al. in PLoS One does a pretty good job of getting around at least some of these problems and is perhaps the most informative study of adult neurogenesis in humans since the original Eriksson study. The authors focus their study on doublecortin (DCX), a protein involved in cell migration and the extension of neuronal processes. DCX can be expressed in mature neurons but, within the dentate gyrus, it is a reliable and specific marker of immature neurons…at least in rodents. Whether or not DCX can be applied to human studies of adult neurogenesis is a legitimate concern. Previous studies have found putative DCX-expressing cells in human tissue but these cells often lacked dendrites and axons and so it was questionable whether these cells were in fact neuronal (or sometimes even cellular).

To get at these issues, Knoth et al. examined a large number of subjects spanning a huge age range (0 to 100 years!). Since neurogenesis declines with age in rodents and primates, if DCX is truly labeling immature neurons, and if neurogenesis in humans parallels that in rodents, you’d expect the numbers of DCX+ cells to decrease with age. And this is exactly what Knoth et al. report: the density of DCX+ cells decreases roughly tenfold from puberty to old age (their Fig. 9), a drop that is pretty similar to what is seen in rodents. It is also nice to see, in their Fig. 4, some pictures of DCX+ cells that actually resemble neurons. Some cells look like those I’ve seen before (e.g. their Fig. 4e,f) but the cells in a 65 year-old subject and those in the 9 day-old subject have dendritic processes and give the reader confidence that this DCX staining is real. Interestingly, Boekhoorn et al. have shown that, as the interval between death and brain fixation increases (which is quite variable in preservation of human tissue), DCX-expressing cells lose their dendritic arbor but do not altogether disappear. Therefore, it is likely that any potential morphologic variability caused by delayed fixation did not prevent Knoth et al. from accurately quantifying DCX+ immature neurons. It is also worth noting that their estimate of the magnitude of neurogenesis, ~1-10 DCX+ cells/mm² in adulthood is in the same ballpark as reported by Eriksson (humans) and me (rats) for BrdU-labeled cells during middle to old age (~100s of cells/mm³).

Using DCX as a marker of young neurons, Knoth et al. then go on to co-label DCX with other markers of proliferating cells and immature neurons, to better characterize the types of cells present in the adult human dentate gyrus, and to further validate DCX as a true marker of immature cells in humans. They found DCX+ cells also expressed various maturational markers previously characterized in rodents (e.g. proliferation: Ki67, PCNA; neuronal lineage: NeuN, calretinin; and many other markers I won’t pretend to understand). It would be nice to see the proportion of DCX+ cells that express the various markers, to see if the DCX population resembles that of rodents (e.g. is the proliferating proportion of a similar size? What about the “mature” proportion that co-expresses NeuN?). Regardless, the number of markers examined is very extensive and is certainly consistent with the idea that these DCX+ cells are adult-generated and go through distinct phases of maturation.

Morphology of DCX+ cells observed by Knoth et al., ranging from immature (left) to mature (right)

Likely due to the limitations of working with human tissue, many findings are qualitative and will have to be definitively answered in the future. For example, the oldest age at which DCX+ cells were still found to be proliferating depended on which endogenous marker of proliferation was used (Ki67 – 38yr, Mcm2 – 65yr, PCNA – oldest age). Depending on the validity of these markers, it is possible that proliferation ends in middle age and that DCX+ cells found in the oldest subjects are the result of a very protracted cellular maturation process. (Species differences in maturation have been found and there are hints that the neurogenesis process may be slower in primates than in rodents – e.g. cell addition and death are delayed, maturation markers may be delayed). Or does neuronal proliferation occur in old age but is simply too low to reliably detect without sufficient sampling? I also I would love to see low magnification pictures to get a better sense of how immunostaining for the various neurogenesis markers in humans compares to that of rodents, but I’m guessing that’s easier said than done.

All in all, this paper is a welcome addition to neurogenesis literature. And any wishes for more pictures or experiments has to be qualified with the disclosure that, until BrdU in injected into humans, I will always be begging for more. While on this topic – is there any way to get another human study that uses BrdU (or similar) to birthdate new neurons, and provide concrete evidence of the magnitude and maturational profile of adult neurogenesis? Once upon a time BrdU was used extensively to both treat and evaluate cancer. Might there be some BrdU-containing tissue hidden away in lab storage somewhere? Alternatively, many great discoveries have been made, and Nobels awarded, in the name of self-experimentation. Is there any good reason to suspect that a single glass of BrdU water is going to harm any of us? Probably could get a great paper out of it. Then again, we might not be alive to read it…or might not have a hippocampus with which to remember reading it, but we’re not doing science for ourselves anyway, are we?

Knoth, R., Singec, I., Ditter, M., Pantazis, G., Capetian, P., Meyer, R., Horvat, V., Volk, B., & Kempermann, G. (2010). Murine Features of Neurogenesis in the Human Hippocampus across the Lifespan from 0 to 100 Years PLoS ONE, 5 (1) DOI: 10.1371/journal.pone.0008809