In a typical middle school classroom, every student is at a different stage on their way to maturity. Some are still growing, going through puberty or developing brain maturity.
Psychology researchers have looked for a universal method to understand how adolescent brains mature and how schools can respond and find ways to diagnose cognitive disorders.
One method popularized in the last decade is called brain age predictions. Using mathematical models, the method provides an accessible and easy-to-interpret reading of how a child’s brain is developing.
Although the technique is trending — potentially even being used in courts to assess whether a youth can be tried as an adult — there’s a considerable amount of risk when it comes to deciphering the data.
In August, Lucy Whitmore, a neuroscience doctoral student published a new paper in one of the top scientific journals, Nature Communications, offering a crash-course guide to the new and powerful tool. Whitmore co-authored the study with Dani Beck, a researcher from the University of Oslo.
“Dani and I didn’t think that Nature would even pick it up,” she said. “We just thought we’d start from the top journal and work our way down.”
Her paper on brain age predictors will now serve as an “instruction manual,” helping open doors for many scientists to further the field of adolescent brain research.
How brain age predictions work
Brain age predictors work by taking MRI images and measurements of a participant's brain and comparing them to data from the Adolescent Brain Cognitive Development Study (ABCD Study). The ABCD study is a massive, long-term research project, recording neurological data of nearly 12,000 children over the course of ten years.
By measuring brain features like thickness, surface area and volume a mathematical model compares a child's brain to those in the ABCD model to estimate their age.
Researchers then compare the estimated brain age to actual age, producing a measure known as brain age gap.
Whitmore explains brain age gap has been linked with mental disorders. Such as studies indicating that brain age gap could potentially be associated with generalized anxiety, depression and other mental disorders.
Understanding the risks of brain age estimates
Whitmore is no stranger to using this method. She’s published multiple papers using brain age predictors, involving studies linking puberty and brain development.
While performing her studies, she noticed certain grey areas which may be confusing for some early researchers using brain-age predictors.
Typically, this method is used in older adults to test cognitive decline and to predict neurological disorders. However, using this method to study younger populations is trickier.
In her paper, Whitmore warns that jumping to conclusions about brain age can be detrimental. “We simply don’t know the longitudinal effects of brain age yet,” she said. “We don’t know if a teen with a less ‘mature brain’ than peers will eventually catch up or that will have permanent implications.”
The future of brain age predictions
Whitmore explains how her mentor, Kate Mills, an associate professor in neuroscience at UO, was called in as an expert testimony in a juvenile court case. She was asked whether it was possible to estimate the defendant's brain age and determine if their brain was mature enough to be tried as an adult.
“There simply has not been enough research for us to fully understand how brain maturity develops in adolescents,” Whitmore said. “We can’t use this model to simplify an extremely complex process. The data can easily be misinterpreted.”
Whitmore also highlights the future benefits of using this model. “There’s lots to be excited about,” she said. “It has potential to be great if used correctly.”
Brain age predictors could potentially help identify individuals who may need early intervention and additional support when it comes to education.
But before that happens, Whitmore explains there is a lot more to be done, and misinterpreting the model could be detrimental.
—By Leo Brown, College of Arts and Sciences