Brain Origami

 Even the earliest neuroscientists wondered about which parts of the brain did what and how did they get their wrinkly shapes. From the perspective of evolution, having a wrinkly brain makes it easier for neurons to communicate to each other by bringing them together. But how do all those wrinkles form? 


The cerebral cortex is the outer layer of the human brain, folding a surface area of 2000cm2 and with almost one fifth of the 100 billion neurons inside of the brain. Each neuron can connect to as many as 7,000 different neurons which means that our brains have to fit an insane amount of connections into a volume a little more than half the size of a 2L soda bottle. In order to fit 140 trillion connections to the surface with as short a wiring as possible, the brain surface efficiently folds into itself making the wrinkly brain that we see everywhere. This process is called gyrification, the way by which the cortex folds itself to maximize surface area to brain volume.


Although the human brain starts off smooth, it begins folding during the second trimester and doesn’t stop growing until adulthood. Many regions are known in the medical field by the individual folds termed gyrus and sulcus. In different people these folds develop at different times and in different ways, but the main gyri and sulci still appear in reliable locations and directions. We know that different parts of the brain play distinct roles in our thoughts and actions, and can use modern neuroimaging techniques to identify brain regions that control these, even if we don’t yet understand everything about how to interpret this.


In regions like the motor cortex, we can use strong magnetic pulses or electricity to map out which portions of the cortex control different muscles, and we can use similar techniques in other regions like the visual cortex to fill in the fundamental roles it plays in how we see. But even though we base so much of what we know based on the brain regions we measure in, there are lots of questions that we still haven’t answered. What is responsible for making these folds? Is it genetics, a cortical blueprint?  If folding goes wrong does it affect brain function?


One theory proposed over 40 years ago suggested a revolutionary idea that way in which the brain folds might be the result of simple mechanical rules. Many scientists studying the way in which the brain folds during development noted that the surface area of the cortex keeps a constant ratio with the volume of the brain. When the brain gets bigger, the cortex gets bigger too, but it no longer evenly covers the brain, and with no room to expand, starts to fold in on itself. Researchers predicted that the growth of the brain could be caused by this constrained growth could be due to differences in the stiffness of the outer layer and the inner layer and the thickness of the cortex. Although this idea was very elegant, it was very difficult to prove and nobody knew how they could. 


Recently, researchers at Harvard discovered a clever way to show the way the brain might form its folds. Using a 3D printer, the researchers printed a model made from a soft-elastomer based on how an unfolded brain looks at 22 weeks old. Then researchers covered the brain in another gel which would swell when a solvent (hexane) was added to the model. Since the size of the brain would stay the same size, they could see what happened when the brain’s outer layer grew faster than the layer underneath!


If you watch the sped-up version of the gel “folding”, you can see how this folded brain seems very similar to how a real brain looks. This experiment represents a beautiful example of how simple laws can cause relatively complicated folding patterns. However, the model itself is still too simple, and while it shows us a lot about how and why the brain starts to fold, there are still many more things that might shape the brain.


So what’s next? Even without knowing everything that will happen when the brain folds, we can still make major steps forward. One measure, the Gyrification Index has already been explored as an important sign in different diseases, including Alzheimer’s, autism, and schizophrenia. Now that we have a mathematical model, we might be able to figure out what causes these relationships, how to detect them earlier, and maybe even how to treat them.


For another great summary of this please check out PhD Comics excellent Youtube video :)


 Tallinen, T., Chung, J. Y., Rousseau, F., Girard, N., Lefèvre, J., & Mahadevan, L. (2016). On the growth and form of cortical convolutions. Nature Physics, 12(6), 588–593.

Caviness, V. (1975). Mechanical model of brain convolutional development. Science, 189(4196), 18–21.

Kuhl, E. (2016). Biophysics: Unfolding the brain. Nature Physics, 12(6), 1–2.