It turns out that the human brain is 10 times easier to break than Styrofoam

The human brain is one of the most incompletely understood organs and holds many mysteries. Research into its abilities is often the focus of attention, but this research clarifies its strength. Our brains break down ten times more easily than Styrofoam, they say.

It was conducted by a team led by Professor Nicholas Bennion of Cardiff University in the UK.

However, this experiment did not actually compare strength by hitting the Styrofoam and the brain with a hammer.

As featured by New Scientist, the team combined machine-learning algorithms with MRI scans in which the patient lay on their stomach, then turned up and shifted the placement of the brain within the skull, to align the brain and skull. Various material properties of the connecting tissue were determined. They measured the ability of the brain to collapse under pressure, its response to lateral pressure, and the elasticity of its connective tissue.

“Brains with nothing preserved are surprisingly rigid and can easily fall apart. The brain is probably much softer than many people think,” said Professor Benion. there is

It turns out that the brain is not only softer than Styrofoam, it is 1,000 times less resilient to lateral pressure than rubber, and is as flexible as a sheet of gelatin.

The MRI study was conducted in collaboration with the Cardiff University Brain Imaging Research Center on 11 subjects (7 males and 4 females) aged 22 to 30 years. After the 20 min prone precondition, only one prone prone picture was taken to ensure that the brain was completely relaxed. Scans were then taken again after transitioning to a typical supine position.

The prone and supine images were first aligned using affine transformation of the skull only, and the displacement of the entire cerebrum was measured. We then created a full-volume vector displacement field in the subject’s personal space by supine-to-deformation registration.

Researchers have long known that the brain is both very soft and very fragile, but the new study is helping to extend that concept to delicate surgery, said Ellen Kuhl, a professor at Stanford University in California. It is said that it has become more accurate to better convey the surgical procedure.

Krystyn Van Vliet, a professor at the Massachusetts Institute of Technology, said the new method may not be able to adequately capture brain deformation during more strenuous exercise than repositioning, such as head injuries from contact sports or car accidents. Say.

The researchers hope to use this model to predict changes in the brain that occur during surgery in individual patients based on preoperative MRI images. This eliminates the need to repeatedly implant the tool into the brain until the proper position is found, and may allow for less invasive surgery.

Research abstract

Computational modeling of the brain requires an accurate representation of the tissues involved. Mechanical testing poses many challenges, especially at low strain rates such as in brain surgery, where fluid redistribution is biomechanically important. In FEBio, we created a finite element (FE) model representing the spring element/fluid-structure interaction of the tibial arachnoid complex (PAC). The model was loaded to represent gravity in prone and supine positions. Statistical software was used to identify material parameters and perform sensitivity analyzes to compare FE results with human in vivo measurements. The results for brain Ogden parameters μ, α, and k were 670 Pa, -19, and 148 kPa, supporting the values ​​reported in the literature. The stiffness of the membrane was 1.2 MPa, and the out-of-plane tensile stiffness of PAC was 7.7 kPa. It turns out that brain displacement is non-rigid and primarily due to fluid redistribution within the tissue. To our knowledge, this is the first study to use in vivo human data and gravity loading to estimate the material properties of intracranial tissue. This model has potential applications for mitigating the effects of brain misalignment in stereotactic neurosurgery.