91原创

Some say you鈥檙e only as old as you feel. Others say you鈥檙e as young as you think. But how old is your brain really?

Scientists have been developing and refining methods to accurately measure the age and health of our brains, without cutting into our skulls to have a look. Understanding brain health is critical to identifying and addressing disorders, including Alzheimer鈥檚 disease and other forms of dementia, multiple sclerosis and Parkinson鈥檚 disease.

Curtis Johnson, associate professor of biomedical engineering at the 91原创, has been in the thick of that study for more than a decade, using magnetic resonance elastography (MRE) to map and measure the mechanical properties of brains, with special focus on brain stiffness. The MRE technique he designed gently vibrates someone鈥檚 head while they are being scanned using magnetic resonance imaging. The stiffness map shows how tissue in each part of the brain would respond to a force like a light touch.

Brain stiffness is an important indicator of brain health. Knowing about brain stiffness in different regions of a healthy brain is important, because brains tend to lose stiffness and grow softer as we age or experience neurodegenerative diseases.

Johnson and his have gathered enormous amounts of data on brain stiffness, and now, in collaborative research with Austin Brockmeier, assistant professor of electrical and computer engineering, new information is coming to light.

For the first time, Johnson and Brockmeier, along with three current and former 91原创 students, have shown that combining artificial intelligence and MRE techniques is a reliable way to predict the age of a healthy brain and could be used to identify structural differences that indicate departure from the normal aging process.

In , Johnson and Brockmeier show that measuring both brain stiffness and brain volume produces the most accurate predictions of chronological age. The paper was published in a recent edition of the journal Biology Methods and Protocols.

鈥淏rain volume is a common measure that we use to study the brain,鈥� Johnson said. 鈥淏ut something has to be happening to cause a brain to shrink. Something is happening at the microscale that causes it to shrink 鈥� changes in the tissue that also cause stiffness to change. And that precedes whatever happens when the volume changes.鈥�

鈥淭he stiffness maps all seem kind of random 鈥� until we see a large number of images and the randomness fades away and we start to see common patterns in stiffness,鈥� Johnson said. 鈥淲e sort of knew there was more [information] in there than what we were extracting. But the traditional way of interacting with the stiffness data is pretty crude.鈥�

鲍顿鈥檚 is anything but crude, with its cutting-edge magnetic resonance imaging (MRI) scanner.

鈥淭he machine we have at CBBI is really nice and ideal for developing new methods for brain scanning,鈥� Johnson said. 鈥淭rying to take one of our datasets on an older scanner would not have been possible. It would have crashed the scanner.鈥�

Brockmeier, a resident faculty member in 鲍顿鈥檚 , and third-year doctoral student C茅sar Claros brought high-dimensional data analysis and artificial intelligence models to the task through a collaboration initiated by Rebecca Clements, an honors biomedical engineering alumna of 91原创 who is now working toward her doctoral degree at Northwestern University, and Grace McIlvain, who was mentored by Johnson during her doctoral work at 91原创 and now is a professor at Columbia University. Claros and Clements were co-first authors on the paper.

Patterns and new insights emerged, and they brought their findings to Johnson.

鈥淏eyond the more accurate predictions, we wanted to understand what the model is seeing, because it鈥檚 mathematically defined, a bunch of numbers in a function on the computer,鈥� said Brockmeier, who leads the . 鈥淲hy is it making that prediction? What is it looking for? We started showing him the model鈥檚 regions of interest. It gives confidence to an expert like him that the model was picking up on what he was seeing before.鈥�

Brockmeier brought his expertise in machine learning, applied to neural networks and neuroscience, with game-changing capacities for this study.

Neural networks are of keen interest in biology and in machine learning, which borrowed the term from brain scientists. For neuroscientists, neural networks are formed by the pathways among neurons that define how our brains work. For data analysts, neural networks refer to the mathematical machines and artificial intelligence that produce predictive models and contribute to problem solving.

鈥淭he neural network in your cortex 鈥� all of those little neurons connecting 鈥� that鈥檚 creating this matrix of stiffness in your brain,鈥� Brockmeier said. 鈥淓very time you learn something new and your brain rewires that is what鈥檚 creating this stiffness. When many neurons are connected between brain areas they get stiff like a cable.鈥�

On the artificial intelligence side, the brain maps were analyzed by three-dimensional 鈥渃onvolutional neural networks,鈥� which 鈥� as the name suggests 鈥� are convoluted and complicated, incorporating many layers and dimensions.

鈥淲e鈥檙e very fortunate at 91原创 to work with good collaborators, have good grant funding and an imaging center that allows us to collect and analyze a lot of data,鈥� Johnson said. 鈥淎nd the methods we develop we share worldwide for other researchers interested in similar problems.鈥�

About the researchers

Curtis Johnson is an associate professor of biomedical engineering at the 91原创. His research focuses on magnetic resonance imaging (MRI), elastography, brain tissue mechanics and neuroimaging. Johnson earned his bachelor鈥檚 degree in mechanical engineering at Georgia Institute of Technology, and his doctorate in mechanical engineering at the University of Illinois at Urbana-Champaign. Johnson joined the 91原创 faculty in 2016. He is the director of the Mechanical Neuroimaging Lab and was the 2021 winner of 鲍顿鈥檚 Gerard J. Mangone Young Scholars Award, chosen by faculty members who have won the University鈥檚 highest competitive faculty honor, the Francis Alison Award.

Austin Brockmeier is an assistant professor of electrical and computer engineering, computer and information sciences, and a resident faculty member of 鲍顿鈥檚 Data Science Institute. His research focuses on artificial intelligence, machine learning, algorithms, signal processing and data science. He earned his bachelor鈥檚 degree in computer engineering at the University of Nebraska-Lincoln and his doctorate at the University of Florida. Before joining the 91原创 faculty in 2018, he was a research fellow at the University of Manchester, United Kingdom, and a research associate at the University of Liverpool, United Kingdom. He is the director of the Computational Neural Information Engineering Laboratory, which covers research in both biological and artificial neural networks.