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Know your spinal cord – Translating neural signals

whole brain and spinal cord dissected

Here we are on day fifty-three, we are nearing the end for sure. I was going to end the series today, but there is at least one more thing that I think will be interesting to cover. As always, you can find all of our posts in the neuroanatomy category, after all there are quite a few now. Today we are going to talk about how the brain and muscles use different signals to communicate. Basically, they speak different languages; let’s talk about what that means.

We’ve covered a lot of things, but this is probably one of my favorite facts about the spinal cord. Before we talk about the spinal cord specifically, we need to talk about the brain and the muscles. Depending on your background this may or may not be something you already know. When we record from the brain using EEG we record from 0-50 (ish) Hz. When we record directly from the brain (ECoG) we find that the most useful frequencies  are around 70-110 Hz. That would be a high gamma band (30+ Hz is called gamma band).

Below is a plot showing what EEG signals look like, they follow a 1/f pattern, where f is the frequency. The plot is showing the power spectral density (how much of the recorded signal is made up of that particular frequency) so lower frequencies are much more dominant than the higher frequencies.

Power spectral density of EEG recording

Power spectral density of EEG recording

Basically from our best estimates, the brain spends most of its time “talking” in low frequencies, but can get as high at 110 Hz, that’s 110 oscillations a second, that isn’t super fast, but it isn’t delta band (0-4 Hz) slow either. Here’s the rub though, even if we compare ECoG, which is super invasive and as close to “ground truth” as we can get without puncturing the brain to get closer to the neurons, it comes nowhere near the high end of EMG (the non-invasive way we record muscle signals).

The dominant frequencies for EMG recordings are around 50-500 Hz, almost 5 times faster than the fastest frequencies the brain uses. Below is a power spectral density plot showing the EMG frequencies. When the brain is talking with itself (other parts of the brain), it uses the same frequency range (0 to ~100 Hz), so how does the brain manage to communicate with the muscles? This is where the spinal cord comes into play and excuse me for being super excited.

Power spectral density EMG

Power spectral density of EMG

We don’t understand how, but we know that the spinal cord performs some sort of non-linear transformation to the information it receives from the brain before it reaches the muscles. In other words, the spinal cord translates the brain signals to something the muscles can understand. Moreover, the spinal cord translates the muscle signals to something the brain can understand!

When we say something is non-linear, we mean that it isn’t a direct 1 to 1 conversion.The graph below shows a few linear plots (left), basically they are nice straight lines which can increase, decrease, or stay the same. If the spinal cord performed a linear transformation the scaling would be the same across frequency bands, if the scale was 10x increasing for example a 1 Hz signal from the brain would become a 10 Hz signal to the muscles, if the brain sent a 50 Hz signal the spine would relay a 500 Hz signal. This isn’t the case, the transformation is non-linear, the plot below (right) shows several different types of non-linear plots, there are far more complex non-linear plots and they can go all different ways, but this is a good example.

Linear vs nonlinear functions

linear functions (left) are straight lines, which can be oriented in different ways (increasing, constant, or decreasing). Nonlinear functions (right) are curves that cannot be described with a straight line, this is just a small sample of some of the shapes nonlinear functions produce.

All this to basically say that the spinal cord understands both the brain and the muscles. It is the universal translator for the body and if we could understand how it does this, it has the potential to be the rosetta stone of neural communication. From a brain-machine interface standpoint this is interesting becuase we could find ways to simulate signals from the spinal cord to the brain, or from the spinal cord to the muscles.

Furthermore, this would be helpful in spinal cord injury, where the communication has somehow altered and/or been impaired. This is one of the main reasons I absolutely love studying the spinal cord. To me, it is a backdoor to understanding the brain. From a technical standpoint, while the spinal cord is super complex, the brain is still the most advanced organ we have. By uncovering the secrets of the spinal cord, we could get a better understanding of the brain and finally discover how to communicate with it more efficiently.

Yeah, as you can probably guess, I’m super excited about all of this. In particular I’m excited about the prospect of being able to help people with spinal cord injury. This is why we use all the “tools” to understand how the spinal cord does what it does.

Hopefully you now have some insight into a key function of the spinal cord that you may not have even realized. One that is particularly exciting for me! I believe (as of now) that tomorrow will be our final post in the series. It’s been a fun run, but it has to end eventually. In any case, this is a conversation for another day.

Until next time, don’t stop learning! 

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