The brain is one of the most critical organs of the body. Being the most critical, it is also one of the most complex organs. This is because of the vastness in scales – extremely soft scale associated with neurosurgery; extremely hard scale associated with skill; extremely slow scale associated with brain development and the extremely fast scale with neuron communications. In brain, there are 86 billion neurons with 100 trillion connections. These numbers are beyond our comprehension and through the area of biomechanics, we try to explain some of them. Yearly, 160 billion Euros are spent in European Union alone on brain traumatic diseases. This provides an obvious motivation to delve into neuroscience and expand the field in areas where we can understand the brain better.
A part of neuroscience that is rising is neuromechanics and comparatively, much less is known in this field of study. Many major brain development, brain mechanisms, and diseases are correlated with their mechanical response of the brain both at the cellular and tissue levels. Jumping into this field is a jump into diving into something new as much of the research conducted is either relatively new or there are numerous knowledge gaps to fill up. An anatomical components of the brain is available in terms of geometry and 3D modelling due to an advancement in computation, however, knowledge in the area of mechanical behaviour of brain tissue is lacking. Computation is important in every engineering field in today’s day and age and it’s the same in brain mechanics, a comprehensive 3D and a finite element model of a brain would largely enhance our capability to understand what this complex human organs consists of. This is challenge in every field of science – model building.
The human brain is not so readily available for experimentation, which is why usually porcine and bovine specimens for two reason. One is the ethical dilemma of not being to study or even buy the human brain. They are also considerably costlier and scientists just have to make predictions based on either a smaller sample of human brain or relying on porcine and bovine specimens as they are readily available nearby. The preference is to use a mammal that represents humans the best. Another reason to use porcine brain tissue is to minimise the post mortem time at testing, which is generally longer for human brains. Different age of pigs can resemble to that of humans, for example, 6-12 months old pigs resemble a 4 year old human having a fully developed brain microstructure. Scientists use samples from animals that best meets their experiment requirements and represent the human brain the most for those conditions.
In the “Biomechanics of brain tissue” by Thibault Prevost et. al. at Massachusetts Institute of Technology, the author is concerned with testing the dynamic behaviour of the porcine bran tissue for different strain rates. Lots of models are available for mechanical behaviour for hysteretic behaviour, rate dependence, nonlinearity, shear and volumetric behaviour), however, no one models tries to tie them all and this is the knowledge gap Prevost is trying to achieve by introducing a model with lesser number of parameter. For the brain tissue, the author creates a stress-strain diagram, very similar to that of metals for different strains. Too high of strain rates cannot be applied because the tissue lose their interstitial fluid and undergo irrecoverable microstructural reorganization/reconfiguration. A reasonable recovery period is needed after a sample of tissue has been tested and this can range from 30 minutes to 12 hours. This model can describe a bigger range of strain and strain rate (0-10s-1) parameters of the brain tissue, which has not been done before. However, after 10s-1 the tissue response starts to stiffen. This model has 8 different parameters and they change from animal to animal. The author has predicted this for porcine brain tissue and it predicts accurately. To check this, he validated the model with various existing samples to check if the model fits or not.
In the “The mechanical behaviour of brain tissue: Large strain response and constitutive modelling” by M. Hrapko et. al. is also concerned with strain and strain rate response of porcine brain tissue. However, since this is an older paper of 2006, they only go up on strain rates till 1s-1. This model predicts the correctly predicts the behaviour during loading and relaxation phases on the tissue but falls short in unloading and recovery. Viscoelastic model is created with 16 parameters. The model predicted in this paper falls short of reaching the sample data. Hrapko predicts that it falls short with a maximum deviation of 12.5%. Even then, it is 50% more compliant than the already present models.
Finally in the “Mechanics of the brain: perspectives, challenges and opportunities” by Alain Goreily at University of Oxford just present the importance of brain mechanics and its relevance in the todays world where there are so many different areas to explore. No matter what field you are in, some tie can always be made with brain mechanics. The author makes connections with solid mechanics, fluid mechanics, electrochemistry, electromechanics, brain development, brain tumours, brain surgery and intracranial pressure, traumatic brain injury and shaken baby syndrome. The best part about the article is that it not describes the connections, provide an extensive literature review and also identifies the numerous knowledge gaps. If a person is lost for what research they want to do, this article can surely convince them that brain mechanics is the field to contribute in.
Two differences I found in Prevost and Hrapko papers that I thought are worth considering. First, as mentioned above, Hrapko consider the strain rate of only from 0.01 to 1s-1. However, Prevost is able to go up to 10 s-1. This might be because of a 4 year gap between the publishing dates of the papers. Prevost used porcine brain tissues of pigs 6 – 18 months brains but Hrapko used only 6-12 month old pigs. It has been pointed out in both the papers that an age of 6 months is necessary for cerebral growth of the pig, so that it can be compared to a developed human brain. However, the upper cap of 12 or 18 months is not discussed in either of the papers. This might have caused the discrepancy in strain rates and the model development of both the models and this is why they might not be comparable.
Coming from a background of mechanical engineering, I had never considered learning about the mechanics in the brain until last semester after I heard about this course. It makes sense to me to have that flexibility for metals, having a table for Young’s modulus or stress and strain, but the same in the realm of tissues is flabbergasting and very exciting. I have never read in so much detail about the brain and never considered how stress and strain could even relate to the brain. At first I thought, we were talking about mental stress/depression but soon I realized that this is a mechanics class. This soon changed. It was interesting to learn about the interstitial fluid diffusion which plays a very important role in measuring whether the tissue is damaged or not. If this can be measured readily, we could identify possible tumours, or injuries by measuring these parameters. It was also really interesting to learn how different a human brain is in comparison to animals, for example, the human brain tissue is 30% stiffer than porcine brain tissue.
In these papers, it was hard for me to follow the mathematics of model development. Mathematics is not a problem for me but generally I lose track of the steps when I read conference and journal papers. It’s also possible that some steps are beyond my capabilities in mathematics at this point in my career. I think this is something I can develop with time to come.