Neurogenesis, the endless puzzle of neuroscience.

When I was a child, my grandma used to tell me that I shouldn’t hit my head when playing because I would kill my neurons and, she would add, we are born with all the neurons that we need. No more neurons are produced as we get older.

Fifteen years later, in my introductory neuroscience course, we learned about the concept of neurogenesis. Neurogenesis is the process of generating new neurons. We learned about the research that challenged the major dogma in neuroscience: no new neurons are produced in the adult brain. This dogma was held by three main factors:

  1. Clinical evidence: adult patients suffering from brain injury do not easily recover.
  2. Networks: how can newborn neurons integrate into the already existing networks of the brain? How can neurons integrate without disturbing the already established information in our brain?
  3. Stem-cells: the importance of pluripotent cells (cells that can differentiate into many other types of cells) had not been recognized yet.


Fernando Nottebohm, an Argentinian neuroscientist working at Rockefeller University, is considered as the first one who provided definitive evidence that there are newborn neurons in the adult vertebrate brain. Since then, scientists have used similar methods to study neurogenesis.  The basic principle is that, because all cells come from other cells and any cell going through division needs to make a copy of its DNA, we can use an analog of thymidine to identify newborn cells. You can think of thymidine as a lego piece that is incorporated into the DNA of replicating cells. Cells can’t differentiate between real thymidine and thymidine analogs (e.g., BrdU), so they incorporate this thymidine analog,  which we can later visualize using antibodies. An important note is that BrdU and any other thymidine analogs allow us to make conclusions about newborn cells, but they do not say anything about the type of cell they are.

In order to identify newborn neurons, scientists add a second type of labeling for neural markers. Mature neurons express proteins that make them neurons instead of, for example, skin cells or glial cells (also found in the brain). Some of these markers are NeuN and calbindin. Therefore, if we find neurons in brain tissue labeled for both BrdU and a neural marker, we can conclude that these are newborn neurons!

Neurogenesis in the adult human hippocampus was first reported by Eriksson and colleagues back in 1998. In their experiment, they studied brain samples from patients who died from cancer and who were previously injected with BrdU (to keep track of their tumor growth). They found newborn cells that expressed neural markers in the hippocampus, a brain region involved in learning and memory.

Last week, however, Sorrells and colleagues reported that “Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults”. In the NPR, they released a podcast called “Sorry, Adults, No New Neurons For Your Aging Brains”. Apparently, we are back to adopting my grandma’s thinking, but after reading the paper, I ended up believing Professor Beltz’s explanation. She studies neurogenesis in crayfish and they discovered that the immune system provides the neural precursors needed to have neurogenesis. In Sorells and colleagues’ paper, they were actually looking at the neural precursors, not at the newborn neurons (at least in the experiment with humans). They couldn’t find a “defined population of progenitor cells […] in the subgranular zone [a region in the hippocampus] during human fetal or postnatal development”. Therefore, they concluded that neurogenesis is not happening in the adult brain because there are not neural precursors in the brain.

However, as Professor Beltz proposes, there might be an extrinsic source of neural precursors: the immune system. Some experiments studying women who received a bone marrow transplant from men support this hypothesis because Y-chromosome-containing neurons were found in their brain! Of course, there are people who think that these results might come from a problem with a leaky brain blood barrier of patients (the brain blood barrier protects the brain and does not let in many extrinsic cells/molecules). Others believe that the neural proteins found in these cells are the product of fusion instead of being produced by transdifferentiation (going from a type of cell to another one). Transdifferentiation is a conflictive idea because neural tissue emerges from a type of tissue (ectoderm) that is different from the tissue that produces cells from the immune system (mesoderm).

I am starting to get very technical and it’s 1:28 am, so I just want to finish this by saying that we might still live in dogma. There are tons of papers out there showing that neurogenesis occurs and that we might be looking at the wrong place looking for the wrong thing because we won’t change the dogma in science. Cells from the immune system might be the precursors of neurons in our brain!


A commentary on Bach speaks: a cortical “language-network” serves the processing of music.

From Course 9.71

A commentary on Bach speaks: a cortical “language-network” serves the processing of music.

Koelsch and colleagues (2002), aimed to investigate the neural correlates of music in this paper. Particularly, they wanted to test whether the cortical network comprising music processing overlaps with the regions involved in language processing. They stated that some temporal and frontal single areas related to language had been found to be involved in music processing, but only for “one-part stimuli” (melodies). The network comprising the areas of both Broca and Wernicke had not been found to be related to music processing yet. Koelsch and colleagues speculated that the use of one-part stimuli in previous experiments was the reason why these areas were not shown to be active in music processing. Therefore, they decided to use multipart stimuli (chords) in a fMRI study to investigate the neural correlates of music with respect to the known “language network” in the human brain.

Koelsch et al (2002) designed four experimental conditions using chord-sequences: in key, clusters, modulations, and deviant instruments. They based their conditions on the principles of Western tonal music (major-minor tonal system). Therefore, the in-key condition was simply a sequence of chords in a major key, the cluster condition was a dissonant tone-cluster, the modulation condition was a sequence of chords in the minor key, and the deviant instruments were major chords played by an instrument other than piano. The subjects, ten non-musicians, were instructed to detect deviant instruments and clusters by pressing a right button when no cluster had occurred since the last response and by pressing a left button for deviant instruments. The researchers designed the experiment in this way to focus the attention of the participants on the tone and the harmonic aspects of the stimuli. In addition, no motor response was present in the detection of clusters and the task-irrelevant modulations were investigated as well.

The analysis of the fMRI-data in this study revealed that the clusters, deviant instruments, and modulations activated a very similar broad neuronal network when compared to in-key blocks. The authors discussed that the regions in this cortical network are also well known to be involved in the processing of language. This result, in combination with some previous studies, led Koelsch and colleagues to conclude that the cortical language network was less domain-specific as previously believed.

The first shortcoming that I noticed in this conclusion is that assuming a correct fMRI-data analysis, there is not enough evidence to say that this “neural activation overlap” means that both language and music processing share the same network. For instance, two physically distinguishable networks of neurons could be spread across very similar regions in the brain without necessarily being the same ensemble. Alternatively, there could be neurons acting as integration centers in these regions for both language and music networks. However, this does not mean that the language network serves to process music.

Moreover, even when the idea of having one network for music and language processing seems reasonable in terms of a system for acoustical analysis of tones, their explanation of musical semantics for the activation of Wernicke’s area in cluster, modulating, and deviant-instrument sequences seems weak. This is because the fluctuations of chords and instruments do not have a predetermined meaning as signifiers in a language do. Similarly, Koelsch and colleagues concluded that the activation of BA 44 was a result of the syntactic processing of dissonant and minor chords. It is true that there are rules in the construction of melodies in western music, but deviant instruments also elicited areas of music-syntactic processing, which makes this a weak argument as well.

With respect to the group analysis of the fMRI data, a one-sample t-test across the contrast images of all subjects was performed. The problem in this type of analysis is that averaging across participants, whose anatomy is not exactly the same, blurs brain activations and can generate a false overlap of closely adjacent responses that do not actually overlap. A better approach, in this case, would be the determination of regions of interest in each individual and then comparing the brains of the subjects. Furthermore, a multi-voxel pattern analysis would be helpful to identify the arrangements of neural activation for language and music in overlapping regions, supporting the idea that there are distinct or similar networks for these two domains.

Another weakness of the analysis in this study is that they did not obtain their own comparative data for language. I think that they should have included tasks analogous to syntax and semantics in language, which is what they were trying to test in music processing, even if such comparisons are debatable. Finally, I think that it would have been better to test their hypothesis of shared networks for music and language by developing an easier task. For instance, the speech-to-song illusion would have been a good start because the stimulus is the same, but the perception changes over time. This task could have also prevented differences in neural response driven by acoustic differences, as it is the case in many of these music-speech studies.



Koelsch, S., Gunter, T.C., v Cramon, D.Y., Zysset, S., Lohmann, G., and Friederici, A.D. (2002). Bach speaks: a cortical “language-network” serves the processing of music. Neuroimage 17, 956-966.

Peretz, I., Vuvan, D., Lagrois, M.E., and Armony, J.L. (2015). Neural overlap in processing music and speech. Philos Trans R Soc Lond B Biol Sci 370, 20140090.

Tierney, A., Dick, F., Deutsch, D., and Sereno, M. (2013). Speech versus song: multiple pitch-sensitive areas revealed by a naturally occurring musical illusion. Cereb Cortex 23, 249-254.


Technique: Fiber Photometry Calcium Imaging

<<La solitude vivifie ; l’isolement tue.>>

Joseph Roux


Paper: Dorsal Raphe Dopamine Neurons Represent the Experience of Social Isolation

Fiber photometry calcium imaging is a method that allows us to measure neural activity in a particular population of neurons by means of genetic and fluorescent recording techniques in freely moving mice. First, luminescent calcium indicators (e.g., GCaMP6m in this paper) are genetically expressed in a desired set of neurons. Next, an optic fiber is surgically placed above the population of neurons expressing the calcium indicator. Finally, the optical fiber works in a bidirectional way. That is, the system of optical fibers excites and records the activity of the calcium indicators thanks to a dichroic mirror and a photodetector. More specifically, the fiber excites the GCaMP6m with light at about 470nm and the recorded activity is compared to the baseline of around 410nm or 430nm of background excitation. Notice that such measurements are the overall activity of the entire set of neurons. In addition, fiber photometry is particularly attractive when we want to record the activity of freely moving mice (unlike other methods that require animals to be head-fixed for calcium recording). In general, this technique is useful when dissecting neural circuits because it allows scientists to implant multiple fibers and record the activity of a particular set of neurons when other populations of neurons are excited as well. The real-time recording ability of fiber photometry permits researchers to compare the activity of different neural populations and see what neurons are related to the orchestration of a particular behavior.

The figure below (figure 2 A, B) shows a schematic and real microscopic representation of how fiber photometry was set up in the freely moving mice in this experiment. The researchers in this paper were trying to study how social isolation affected the activity of dorsal raphe nucleus (DRN) dopamine (DA) neurons. In order to do this, they had two experimental groups. One group of mice was socially isolated while the other one was allowed to hang out with other mice. Of course, these mice were previously injected with adeno-associated viral vectors to express GCaMP6m in dopamine neurons of the DRN. Then the individual mice in the fiber photometry set up were presented to a young mouse. The fiber photometry recordings showed that isolated mice had a significantly greater fluorescent response to this new mouse (Figure 2 C). Furthermore, in order to avoid any possible confounds, the authors compared this neural activity to a control: a new object instead of a new mouse in the same experimental setup. Indeed, the response of isolated mice in fiber photometry was greater for the new mouse than for the new object. The authors concluded that, after experiencing social isolation, DA neurons in the DRN have a significant greater activity when exposed to a social stimulus. Of course, this is just a correlation because fiber photometry does not allow us to make conclusions about causation. Optogenetic manipulations would be necessary to corroborate causation.

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Matthews Gillian A, Nieh Edward H, Vander Weele Caitlin M, Halbert Sarah A, Pradhan Roma V, Yosafat Ariella S, Glober Gordon F, Izadmehr Ehsan M, Thomas Rain E, Lacy Gabrielle D, Wildes Craig P, Ungless Mark A, Tye Kay M Dorsal Raphe Dopamine Neurons Represent the Experience of Social Isolation. Cell 164:617-631.

Commentary on “A Cortical Circuit for Sexually Dimorphic Oxytocin-Dependent Anxiety Behaviors” (Li et al., 2016)

NEUR 315

Heankel Oliveros

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Article: Li, K., M. Nakajima, I. Ibanez-Tallon and N. Heintz (2016). “A Cortical Circuit for Sexually Dimorphic Oxytocin-Dependent Anxiety Behaviors.” Cell 167(1): 60-72.e11.

The authors of this paper investigated a group of cortical neurons that modulate both social and emotional states differently in male and female mice. Previously, these researchers identified oxytocin receptor interneurons (OXtrINs) in the medial prefrontal cortex (mPFC) and discovered that these neurons facilitate social behavior in females during their estrus phase by acting on layer 5 pyramidal neurons. In contrast, despite males and females having an equivalent number and distribution of OXtrINs, the photostimulation of OxtrINs or administration of OXT in the mPFC had no effect on the social behavior of male mice.

In this study, the researchers focused on the regulation of anxiety-related behaviors by OXtrINs in males and females. They approached this question by photostimulating OXtrINs expressing channelrhodopsin. They transfected these neurons by doing a bilateral stereotactic injection of Cre-dependent AAV virus encoding channelrhodopsin in the mPFC of Oxtr-Cre mice. Then these mice were tested in three behavioral tasks. The first one, the three-chamber social interaction test, examined the preference of animals to spend time with a novel object or a new mouse. The other two tests, the open field (OF) and the elevated plus maze (EPM), aimed to assay the anxiety of mice. Consistent with previous experiments, females displayed social preference when OXtrINs were activated using blue light. In contrast, the stimulation of OXtrINs had no changes in social preference in male mice. On the other hand, when OXtrINs were stimulated during the EPM and OF tests, males showed a decrease in anxiety-related behavior. That is, they spent more time in the center arena of the OF and explored open arms more frequently. Females, however, did not display any changes in behavior upon OXtrINs activation.

Once the authors confirmed that OXtrINs were candidates for the modulation of anxiety-related behaviors in male mice, they proceeded to determine whether the anxiolytic effect of these neurons required OXT/OXTR signaling. To test the necessity of oxytocin signaling, the authors injected an AAV-expressing Cre to delete the OXT receptor gene (Oxtr). The controls were injected with AAV-expressing GFP. After behavioral testing, the authors observed a strong anxiogenic effect in male mice whereas controls and females did not show any changes in anxiety-related behavior.

In the second half of the article, the authors investigated the pathway by which OXtrINs activation modulates local neurons to produce anxiolytic effects. They approached this question by doing whole-cell recordings of neurons in layers 2/3 and 5 during photostimulation of OXtrINs using optogenetics. The authors discovered that males had a larger inhibitory postsynaptic current (IPSCs) in 2/3 pyramidal neurons, whereas females showed larger IPSCs in layer 5 neurons. In terms of excitatory postsynaptic currents (EPSCs), neurons in layer 2/3 were similar for both males and females, whereas layer 5 neurons displayed larger amplitudes for females. The conclusion from this experiment was that the activation of GABAergic OXtrINs results in different inhibitory effects in the local circuit activity of females and males.

Next, the authors gained more insight into the signaling pathways in these circuits by examining the translated mRNAs in OXtrINs. To analyze OXtrINs-specific mRNAs, they used a technique called TRAP (Translating Ribosome Affinity Purification). In short, TRAP works by isolating EGFP-tagged ribosomes and examining the mRNA in them. TRAP localized cell-specific ribosomes by expressing EGFP under a conditional promoter in Oxtr-cre mice. After sequencing the obtained RNA, the authors focused on the top ten most highly enriched translated mRNAs in OXtrINs. From these top candidates, the researchers decided to focus on corticotropin-releasing factor-binding protein (CRHBP) because it binds to corticotropin-releasing hormone (CRH) with high affinity and inactivates it action on CRH receptors.

After discovering a high expression of Crhbp in OXtrINs, the authors inferred that the anxiogenic action of CRH on the mPFC could be regulated by the production of CRHBP when OXtrINs are activated in males. To test this hypothesis, the authors performed electrophysiological recordings in layer 2/3 pyramidal neurons and applied a CRH bath to female and male slices. The results showed that male slices treated with CRH had an increase in action potentials. These CRH-generated spikes were suppressed by co-application of CRHR1 antagonist. In contrast, the spiking activity of neurons in female slices had a very small increase that was insensitive to CRHR1 antagonist. In addition, layer 5 neurons were insensitive to CRH treatment in both males and females. This experiment demonstrated that male layer 2/3 neurons were more sensitive to CRH compared to females and that the activation of Crhr1 receptor was responsible for this neural activity. Next, the authors performed electrophysiology again from layer 2/3 neurons, but this time they stimulated OXtrINs using optogenetics. The recordings showed that the CRH-induced activity of male slices strongly decreased during OXtrINs photostimulation. Taken together, these results demonstrated that the activation of OXtrINs may decrease the response of layer 2/3 neurons to CRH.

Finally, the authors used a conditional knockdown of crhbp in OXtrINs by injecting a lentiviral construct to express shRNAs for Crhbp in Cre-positive OXtrINs only. They had controls expressing EGFP instead of the shRNA cassette. Then the animals were tested in the OF and EPM tests. The results demonstrated that anxiety-like behaviors were not affected in transfected female mice whereas males did show an increase in anxiety-like behavior compared to controls. In addition, female sociosexual behavior was not changed by this shRNAs knockdown. These results suggest that CRHBP synthesis from OXtrINs regulates anxiety in males only. Considering their electrophysiological recordings, the anxiogenic action of CRH in layer 2/3 neurons is likely to be decreased by the production of CRHBP from OXtrINs in male mice. The authors conclude by suggesting that the higher CRH levels in females compared to males might be responsible for the insensitivity of these cortical neurons to the production of CRHBP from OXtrINs.

In general, I think that this article includes good controls and behavioral tests that support their cortical model for oxytocin-dependent anxiety behaviors. For instance, they tested females during different phases of their estrous cycle to make sure that the anxiety-related behaviors regulated by this cortical circuitry were not dependent on other hormonal levels. In addition, they used two tests for anxiety-related behaviors to support their conclusions and they also examined the sociosexual behavior of animals whenever they did a genetic manipulation.  Moreover, their CRH hypothesis is supported by their experiments (electrophysiology and TRAP/RT-PCR) because they showed that 1) CRH did not change the neural activity of pyramidal neurons in the mPFC of females, and 2) CRH levels are higher in females. The only limitation that I see in this study is that the anxiolytic effects of this circuitry were not tested in fear conditioning. One could argue that the findings in this study are not related to anxiety, but rather to an impairment in risk assessment, so the animals are more likely to explore the open arms and the center of the open field because they do not process the risk that such actions imply. In fact, the mPFC is also involved in risk assessment (Xue et al., 2009). By testing this circuitry in fear conditioning, we could eliminate the confounding variable of risk assessment because animals have already learned to fear a stimulus. However, the involvement of CRH in this circuit is a strong indicator that this is indeed an anxiety-related neural circuit.

In future experiments, the authors could investigate how the levels of CRH might mediate sex differences in this circuit. One approach would be to regulate CRH levels by targeting Crh in the paraventricular hypothalamus, which was reported to have a very high expression of CRH in females. The authors could perform a conditional knockdown of this gene by using a similar siRNA technique as they did on this paper. They could put their CRH-siRNA under a tissue-specific promoter for the targeted cells in the hypothalamus. They could test females (OF and EPM) early in development and in adulthood in order to see if there is a critical period for the formation of this sexually dimorphic anxiolytic circuit. This type of experiment would allow us to explore how CRHBP is able to have different regulatory actions on cortical circuits that do not have any evident differences in neuroanatomy and mRNA profiles related to CRH.



Li, K., M. Nakajima, I. Ibanez-Tallon and N. Heintz (2016). “A Cortical Circuit for Sexually Dimorphic Oxytocin-Dependent Anxiety Behaviors.” Cell 167(1): 60-72.e11.

Xue G, Lu Z, Levin IP, Weller JA, Li X, Bechara A (2009) Functional dissociations of risk and reward processing in the medial prefrontal cortex. Cereb Cortex 19:1019-1027.