MRI and Multidimensional Brain Measurements Can Calculate Child’s Age

A US team of researchers has developed a multidimensional set of brain measurements that, when combined together, can effectively assess a child’s age with 92% accuracy.

“We have uncovered a ‘developmental clock’ within the brain--a biological signature of maturation that captures age differences quite well regardless of other kinds of differences that exist across individuals,” said first author Timothy T. Brown, PhD, a developmental cognitive neuroscientist in the department of neurosciences at the University of California (UC), San Diego (USA) School of Medicine.

This study of the anatomy of the developing human brain planned for publication in the September 25, 2012, issue of the journal Current Biology, addresses a long-standing scientific query about individual biologic variability in children. The findings revealed that, for specific structural measures, maturational differentiations in the developing human brain are much smaller than was earlier thought--providing the first merged profile of different phases of brain development from ages three to 20 years.

Dr. Brown and lead investigators Terry Jernigan, PhD, professor of cognitive science at UC San Diego and Anders Dale, PhD, UC San Diego professor of neurosciences, collaborated with colleagues nine other major US universities. The study, the first published research from the group’s Pediatric Imaging, Neurocognition, and Genetics Study (PING), was supported by a two-year stimulus grant from the US National Institutes of Health (Bethesda, MD, USA).

The researchers developed a structural set of brain measurements including features of neuroanatomic development such as the shape, size, and tissue properties of various parts of the brain, resulting in what the researchers call a multidimensional phase metric.

The investigators exploited on major developments in structural magnetic resonance imaging (MRI), employing a standardized MRI protocol implemented at nine different institutions, using 12 different MRI scanners. Data were collected in a diverse sample of 885 typically developing children and teens, including information on anatomical brain features known to change over age; for example, measurements of cortical thickness and area, the volume of deep structures, tissue characteristics and signal intensities.

“No individual measurement closely reflects a child’s age across the childhood years, because changes of different kinds cascade dynamically as development progresses. But taken together, the measurements reveal a phenotype that is tightly linked to the child’s chronological age,” said Dr. Jernigan. She added that this collection of multimodal, multisite imaging developments now makes it possible for researchers across institutions to establish large-scale, shared databases.

“Since this multidimensional MRI method is entirely objective, quantitative, and noninvasive, and can powerfully distinguish among different phases of maturity, we hope it might also be useful for the early detection of developmental brain disorders,” said Dr. Brown.

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