Birthing new neurons at night

By now it's well established that adults can grow new neurons.

Growing Neurons (source)

But how, when and why these neurons grow is currently under investigation. A 2008 paper attempts to answer the 'when' of neurogenesis. They labeled (PH3) cells in the mouse hippocampus (dentate gyrus to be specific), and counted how many cells were currently going through mitosis at different times of day. They found that during the dark phase, more cells were PH3-positive, indicating that more cells were growing at night.

They also tested whether neurogenesis was modulated by exercise. And it was. Mice who had access to a running wheel in their cage grew about the same number of cells during the night, but grew more cells during the day. So much so that the difference between night and day disappeared.

Tamai et al.,, 2008 Figs 1B and 2D

This figure shows the light-dark cycle (Zeitgeber time) and the number of 'growing' cells. B shows the pattern for control mice, and D shows the pattern for the running mice. Notice that the y axes are scaled differently.

So exercise helped new cells grow, but without exercise more cells grew during the night time. Now all this use of the phrase 'night time' might make you think that this neural growth is happening during sleep.

After a long night of wheel running, Jasper succumbs to a restful days sleep. (source)

But it's not. Mice are nocturnal. They sleep during the day and are wide awake at night. The paper shows that almost all the running that occurs on the running wheel happens at night. So the enhanced cell growth is happening when the mice are active. Why exercising at night causes cells to grow during the day is interesting, but the authors offer no mechanism for why that might be happening.

© TheCellularScale


Tamai S, Sanada K, & Fukada Y (2008). Time-of-day-dependent enhancement of adult neurogenesis in the hippocampus. PloS one, 3 (12) PMID: 19048107

why you shouldn't wash your brain with soap

I can't think of any situation where you might be inclined to soap up your brain (except maybe if you had recently been trepanned), but it is still a bad idea.

you can actually buy soap shaped like a brain here. (It smells like bubble gum!)

When used on say, an oily frying pan, soap + scrubbing will trap the oil in little units which can be rinsed off. Without soap, using only water, the oil which is hydrophobic (meaning it would rather stick to anything besides water) will stick to the pan rather than the water. 

Soap + shaking = trapped oil (source)

How does this relate to the brain? Well the cell membrane which helps give the shape to the neurons is made up of a lipid bilayer. These lipids have a hydrophobic tail (which hides in the middle of the layer) and a hydrophilic head which faces outward, just like the oil particles above.

Cell membrane (source)

So basically if you scrubbed your brain cells with soap, the membrane that holds the neuron together would be disrupted. Scientists actually use this principle to get stuff (like DNA) out of a neuron. In DNA extraction, there is a lysis step in which a detergent (like SDS) is applied to the tissue and given a good shake. This disrupts the membrane and allows access to the contents of the neuron.

You can wash your skin with soap because the living skin cells are protected by an outer dead skin cell layer. Though if you soap up too much, you can actually dry out yours skin by stripping it of lipids faster that they can be replenished. See "How much should you shower" for an excuse to stay in bed tomorrow morning rather than get up and shower.


© TheCellularScale

How high are you exactly?

Your brain might not be sure.

Spiral stairs at the Vatican (I took this picture)

In a study out last year, Hayman et al., (2011) investigate whether the classic place cells and grid cells of the rat brain also encode vertical height. 

We've discussed place cells before, so read this if you want to get back to the basics. Grid cells are a sort of extension of place cells.  They are cells that fire in a regular pattern over an area while you move around in it. 

 

like this (source)

The red dots represent when the neuron fires and the black line represents the path that the animal (probably a rat) was traversing.  As you can see the neuron fires when the rat reaches any of the points that make up a regular grid.   

But this is just the rat crawling around on a flat surface. What happens if you have the rat move vertically?  Does a vertical grid show up? Hayman et al. tested exactly that by introducing the rats to the exciting world of rock climbing.

Figure 1E Hayman et al., 2011

 

While the rats were climbing around on this rat-sized rock wall, the cells that had fired in a grid pattern on a flat surface actually fired in a striped pattern on the pegboard. 



Figure 2A Hayman et al., 2011



On the left is the cell firing like a normal grid cell on a flat surface, but on the right a grid cell (not the same one) is firing in a striped pattern on the vertical climbing wall.

The authors suggest that this might be just the normal grid showing up but extending along the vertical plane. In other words, each point of the grid includes the space directly above and directly below it and basically forms a grid of columns. 

This finding could mean a number of things:

1. The brain does not encode vertical space very specifically.

2. Vertical space is encoded, just not in the hippocampus and entorhinal cortex (where place cells and grid cells reside).

3. A rat's brain doesn't encode vertical space, but maybe brains in other animals (flying animals for example) do.

In a mini review of this paper, Savelli and Knierim (2011), suggest that future experiments on flying mammals known to have grid cells (such as bats) would shed light on the third point. 

Vertical grid cells in 'the flying squirrel'? (source)

I agree and I also wonder if the entorhinal cortex of humans could develop three dimensional grid cells under certain conditions.  Could people who really need to know where they are in vertical space, such as trapeze artists or gymnasts, develop a more specific sense of height?

© TheCellularScale
 

Hayman R, Verriotis MA, Jovalekic A, Fenton AA, & Jeffery KJ (2011). Anisotropic encoding of three-dimensional space by place cells and grid cells. Nature neuroscience, 14 (9), 1182-8 PMID: 21822271

Savelli F, & Knierim JJ (2011). Coming up: in search of the vertical dimension in the brain. Nature neuroscience, 14 (9), 1102-3 PMID: 21878925

Neurons are like Fireworks

Neuron Firework (source)

In honor of American Independence, today here are some beautiful pictures of neurons that look sort of like fireworks.

(source)

And some actual neuroscience studies using light-up cells.  One example, I've already covered is the imaging of neurons as they fire. Since neurons fire really fast, you can see them light up under the microscope.



Computational neuroscience can lead to fireworks as well.  When neurons fire in an artificial network, they light up in beautiful patterns:  (It really sparkles at 2:00)




 © TheCellularScale

Neurons are like Magnets

The earth has magnetic poles just like this magnet

It has long been thought that animals can use the earth's magnetic field to know where they are with respect to the planet itself. Migrating whales and turtles could use this method to determine which direction to swim, and pigeons could use this to navigate over long distances.

Loggerhead hatchling: brain still developing

Recently a paper out of Baylor college of medicine has shown the neural correlates which underlie this magnetic sense. They actually recorded from individual neurons while manipulating the surrounding magnetic field.

Specifically, Wu and Dickman (2012) recorded from electrodes implanted in the pigeon brain stem while the pigeon was sitting in a fully manipulable magnetic field. Wu and Dickman cancelled out the earths magnetic field and then applied a specific magnetic stimulation to the bird's head. After the last test stimulation, the brain stems were stained for c-fos. C-fos is an immediate early gene and is an indicator of activity in a neuron.  In other words the cells that show c-fos after stimulation are cells that were active during the stimulation. (For more on the use of immediate early genes see: erasing memories cell by cell.)

Wu and Dickman, 2012 Figure 2

They show the recording sites (red stars) and the c-fos positive neurons for all the pigeons in C. B is an example (dark dots are c-fos neurons). This brain stem diagram might look familiar, we've talked about the bird brain stem here before regarding sound localization. It seems that birds do a lot of spatial localization with their brain stem.

Wu and Dickman not only found that many neurons in this brain area expressed c-fos after magnetic stimulation, but they also recorded the actual spiking activity of these neurons during the magnetic stimulation.  They stimulated using small magnetic fields in the micro-tesla (uT) range.  For reference a typical MRI machine has a magnetic strength of 3 tesla or so (as in 3,000,000 uT).  They found 53 neurons in the recording area were sensitive to magnetic stimulation (but 276 were not), and that these neurons were sensitive to many types of signal modulation.

"We have shown that single vestibular brainstem neurons encode the direction, intensity, and polarity of an applied magnetic field...Our findings demonstrate that MR neurons are most sensitive within an intensity range that is naturally produced by Earth’s magnetic field, a necessary condition for a magnetoreception system to be useful in the derivation of geopositional information. However, Earth’s magnetic field varies over time (for instance, there has been a 35% decrease in its strength over the past 2000 years), so it would seem likely that magnetoreception systems adapt to the slowly changing fields through evolution and/or developmental plasticity in order to maximize magnetic sense perception." Wu and Dickman, 2012

This is an exciting paper that answers longstanding questions, but also raises new ones (as most exciting science does). For example, how is the magnetic field actually sensed by the pigeon? It is likely that these magnetic response (MR) neurons are receiving input from a sensory organ, and they probably wouldn't be magneto-sensitive on their own. Though an interesting experiment would be to culture these neurons (or the neurons of the putative magneto-sensory organ) and record their sensitivity to direct magnetic stimulation.

Darwin's pigeons

It would also be exciting to test changes in magnetic sensitivity of these neurons based on exposure.  If a pigeon is raised in a completely magnet-free area with the earth's magnetic field actively canceled out, would it lose its ability to detect the magnetic field? Or perhaps in that case, these neurons would become sensitized and respond more strongly to a magnetic stimulation. And how are these magneto-sensitive areas of the brain altered between bird species?  So many exciting questions!



© TheCellularScale



Wu LQ, & Dickman JD (2012). Neural correlates of a magnetic sense. Science (New York, N.Y.), 336 (6084), 1054-7 PMID: 22539554

 

Neurons are like equations

The brain of the clock (I took this picture)

A computational model is a surrogate version of something usually made on a computer.  An example that most people are familiar with are the computational models used to predict the weather. If you know how low pressure and high pressure fronts interact, and you know where one is and how fast it is moving, you can program software to play the situation out in a simulation, predicting what will happen and how quickly. 

Computational neuroscience is more or less just like that and it can be used to investigate all levels of neuroscience. Here's a brief intro to three of the basic levels. There are other types of computational models in neuroscience, but these three make up most of them.

The Whole Brain
If you know how the thalamus, hippocampus, amygdala, and cortex all work together, you can simulate how inputs into one structure might influence the others. In this case each brain structure would basically be a 'black box' that received input and produced output based on known data.  To do this kind of simulation you wouldn't actually simulate the millions of neurons in each structure.

The Neural Network

(source)

On the next level down, you can make a computational model of a neural network inside a single brain structure. If you know the types of neurons in the amygdala and how they interact with each other, you can program those relationships in and test what might happen if one class of neurons fires too much or too little. You can test the effect removing one class of neurons has on the whole network and the output of that brain structure.  In this case you are simulating individual neurons, but you are probably not simulating the details of the neurons, such as their dendrites and their specific channel composition. In this kind of computational model, the neurons are the 'black box' which receive input and produce output based on pre-set equations.

The Cellular Scale
One level down from this is a computational model of an individual neuron. In this type of model, the neuron is simulated in detail, with its dendrites, soma, and sometimes the axon.  With this kind of model, you can test the effects of different dendrite shapes on the processing of the neuron. Usually the individual channels (such as calcium, potassium and sodium channels) in the neuron are programmed in and the electrical properties of the cells are calculated in detail. In this situation, the specific proteins and channels are the 'black boxes' computing ionic concentrations based on pre-set equations. A detailed tutorial on how to make a biophysically realistic model neuron can be found here.

a neuron can be simulated as a series of resistors and capacitors

Sidiropoulou et al., (2006) have an excellent review  of the neuroscience discoveries that have been made with this cellular level of computational modeling.

They start their paper highlighting the most interesting problem in cellular neuroscience.

"Understanding how the brain works remains one of the most exciting and intricate challenges of modern biology. Despite the wealth of information that has accumulated during the past years about the molecular and biophysical mechanisms that underlie neuronal activity, similar advances have yet to be made in understanding the rules that govern information processing and the relationship between the structure and function of a neuron." (Intro, Sidiropoulou et al., 2006) (red mine)

This paper directly argues against the idea that neurons are just 'on-off' switches, and illustrates the complex computational processes that occur in individual locations of the neuron. They cover computational studies analyzing the information processing that occurs in the dendrite, at the synapse, at the soma, and even in the axon. The details are to complicated to get into here, but the paper is free.

Finally, they end with a call to action for experimental and computational neuroscientists to work together to solve the really interesting problems in cellular neuroscience. 

"The following open questions could provide fertile ground for collaborations among molecular biologists, geneticists, physiologists, modellers and behaviourists for further explorations of the mysteries of the brain. Do specific behaviours require certain neuronal computational tasks? Which parts of the neural circuit or the neuron itself are responsible for these tasks? What are the underlying molecular mechanisms for the distinct operating modes of neuronal integration? Such holistic approaches should lend support to the growing idea reinforced by this review: that something smaller than the cell lies at the heart of neural computation." (Discussion, Sidiropoulou et al., 2006)

Just as computational models can predict weather patterns with some degree of accuracy, no model is perfect.  Similarly computational neuroscience is not going to lead to all the answers, but where it is particularly useful is in making very specific predictions about how certain aspects of a neuron or neural circuit might work. The insight gained from computational models can guide and focus experiments, making them more efficient. This saves time, money, energy, and animal lives.

© TheCellularScale


Sidiropoulou K, Pissadaki EK, & Poirazi P (2006). Inside the brain of a neuron. EMBO reports, 7 (9), 886-92 PMID: 16953202

Neurons are like footballs: Special Superbowl Post

Specifically, bipolar neurons are like American footballs.

There are many types of neuron in the brain and they are often classified by shape.
The bipolar neuron has outgrowth on either side of it, often one side has the axon and one side has the dendrite. Sometimes the two sides of the bipolar neuron are both dendrites.  Either way the two extensions off the sides of the cell elongate it, making it look....

Bipolar neuron, Source

like a football!

This is a paper cut-out football used for decorating for superbowl parties

There are many reasons for the bipolar cell to be ovoid-shaped, and I plan to blog about specific neurons in this category in the future and how their shape matters for what they do.  But the real question is, why is the football ovoid-shaped?  Why not a sphere like almost every other ball-game?

Well apparently it is because originally (for rugby, which later translated to American football) the ball was a leather-covered pig's bladder. 

inflated pig bladder (source)

As you can see, the pig bladder is sort of ovoid in shape.
Further refinements on the shape, like the pointed ends and the elongated shape came afterwards, but the oval rather than spherical nature of the original ball is likely based in pig biology. 

This rugby and football related information was from here and here.

Neurons are like Power Cords

Neurons are like power cords because the work by communicating electrical signals from one place to another. 

A neuron has 3 main parts, the dendrites (B), the cell body, or soma (C), and the axon (A).
 

 The dendrites receive the input from other cells.  This input causes an electrical change in the cell, either pushing the cell's voltage upwards (exciting the cell) or downwards (inhibiting the cell).  That signal has to travel down the dendrite to the soma (C).
The dendrites act like leaky power cords, and the strength of the signal decays during its journey to the soma. 
In certain cells, the shape of the dendrite contributes to how it integrates multiple signals and how strongly it sends these signals to the soma.

The axon is responsible for sending out information from the neuron. It is usually longer that the dendrites because it often is carrying a signal from one brain region to another, from one hemisphere to the other, or even from the sensory neurons (like touch receptors) to the spinal cord.
When the axon is very long, it can't work if it is leaky. It will often have a myelin sheath that insulates* it and allows it to propagate its signal with great efficacy and speed.

Some ways that neurons are NOT like power cords are:
  1. Power cords conduct electricity through the jumping of electrons, the current in neurons is due to the movement of ions (like sodium (Na+), potassium (K+), calcium (Ca2+), and chloride (Cl-)), and is not as fast as direct electron movement.
  2. Power cords conduct huge amounts of electricity compared to neurons. The potentials relevant for neurons are on the millivolt scale, and the currents are measured in picoAmps. You could never be shocked by touching a neuron. 

*it is more complicated than just acting as insulation, see Nodes of Ranvier if you really want to know how it works.

Neurons are like Snowflakes

One of the first things that people noticed when looking at the brain on the cellular scale is that neurons are shaped differently from other cells.

Drawings like this one by Ramon y Cajal show that neurons are not only shaped differently from say, blood cells, but also shaped differently from each other. (I am not going to give you a history lesson, but you can read all about Ramon y Cajal and his famous drawings here.) And if you are really lost and don't know what a neuron is, check out neuroscience for kids.

Now there are much more sophisticated ways to analyze the shape of neurons. Software such as Neuromantic allow scientists to digitally reconstruct neurons and quantitatively analyze their shape. Once a neuron is digitally reconstructed, it can be deposited in a database for everyone to use. You can browse at least 7,000 neurons at neuromorpho.org, visualize them in 3D, and even analyze information about how long the dendrites are, how many dendrites branch off, and how much area is in each compartment.

The interesting thing about the shape of neurons is that though they might all be unique (like snowflakes), they can be classified into any number of categories based on their details.  There are certain classes of neuron that could never be confused with one another.  for example, the Purkinje cell of the cerebellum has a distinct sea-coral shape...

purkinje cell

sea coral

cortical pyramidal cells

 ... while the Pyramidal cells of the cortex have a bushy basal arbor of dendrites and a long vertical apical dendrite. 

There are two main types of question currently being investigated by researchers:

1. what genes and molecules make neurons grow into a certain shape
 (review: Libersat and Duch, 2004)? Similarly, how are neuron shapes altered in neurological diseases?

and

2. What does a neuron's shape have to do with it's function?
What sort of information it can receive and communicate when it has a huge flat dendritic arbor (like the Purkinje cell), or when it has a long narrow dendrite (like the Pyramidal cell)? Or could many different neuron shapes function the same way and these differences are just accidents of evolution (as a snowflake's shape is not really for a purpose, but an accident of condensation)?


Feel free to leave answers, questions, and wild hypotheses below in the comments section.