MRI brain scans can accurately predict young person's age
Using MRI technology and mathematical analysis, researchers at Oregon Health & Science University and Washington University in St. Louis are now able to accurately predict a young person's age simply by studying their brain scans. The research, which will likely have several clinical applications, including assessment and diagnosis, is published in the current edition of the journal Science.
For several years, OHSU researcher Damien Fair, Ph.D., and his colleagues at Washington University, Nico Dosenbach, M.D., Ph.D., and Bradley Schlaggar, M.D., Ph.D., have been studying development of the brain using a technology called functional MRI. Traditional functional MRI allows for brain images to be taken while a person is performing an activity. However, in this instance, the scientists use the method to examine the brain is idle or at rest.
'Machine learning'
Previously, Fair used this information to demonstrate how brains develop throughout life. In short, when we are young, brain activity is more localised in the brain. However, as we develop, these connections in the brain become more complex and distributed - much like the way a city's transportation system becomes bigger and more complex as the city grows.
"By utilising this approach along with complex mathematical analysis, called machine learning, we found that we could create a form of brain development yardstick, or what Dr. Dosenbach calls a maturation index," said Fair, a postdoctoral research scientist in psychiatry, OHSU School of Medicine. "Using this yardstick of sorts, we learned that you could effectively determine the subject's level of brain development."
Assisting comparing brain function
The researchers hope that, upon further development, the technology will assist in comparing brain function across populations to assess childhood development during the aging process. For instance, a percentile scale could be developed to gauge brain development much in the way weight and height percentiles are calculated for growing children. Such a tool could highlight individual needs and lead to specific ways of helping individual children.
In the future, this form of analysis may also play a key role in diagnosing childhood developmental delay, ADHD and autism.
"In many cases it can be very hard to diagnose and properly characterise these problems, which is why we are so encouraged by these findings," added Fair.
Source: Oregon Health & Science University
