(HealthDay News) – Using measurements of more than 200 structural features of the brain, a child’s age can be predicted with >92% accuracy.
Timothy T. Brown, PhD, from the University of California San Diego in La Jolla, and colleagues performed multimodal structural magnetic resonance imaging at nine institutions on a diverse sample of 885 typically developing children and adolescents (aged 3–20 years). Measurements were taken from T1, T2, and diffusion-weighted imaging of 231 structural brain features known or suspected to change with age, including brain morphology, signal intensity, and water diffusivity within different tissue types.
The researchers found that, although there was great developmental variability in individual neuroanatomical measures, when taken together they could predict a child’s age with >92% accuracy. This composite measurement also showed developmental differences of only about one year among children of the same age.
“In conclusion, our study shows that noninvasive, imaging-based biomarkers can be used to assess different phases of human brain maturity, producing a highly precise biological metric of an individual’s age,” Brown and colleagues write.
One author is a founder and holds equity in CorTechs Labs.