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Summary: A deep learning model uses white matter connectomes generated from MRI images taken at birth to accurately predict an infant’s cognitive development at age two.
Researchers at the University of North
Carolina School of Medicine used MRI brain scans and machine learning
techniques at birth to predict cognitive development at age 2 years with
95 percent accuracy.
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| The study used an application of artificial intelligence called machine learning to look at white matter connections in the brain at birth and the ability of these connections to predict cognitive outcomes. The image is in the public domain. |
"This prediction could help identify children at risk for poor cognitive development shortly after birth with high accuracy," said senior author John H. Gilmore, MD, Thad and Alice Eure Distinguished Professor of psychiatry and director of the UNC Center for Excellence in Community Mental Health. "For these children, an early intervention in the first year or so of life -- when cognitive development is happening -- could help improve outcomes. For example, in premature infants who are at risk, one could use imaging to see who could have problems."
The study, which was published online by the journal NeuroImage, used an application of artificial intelligence called machine learning to look at white matter connections in the brain at birth and the ability of these connections to predict cognitive outcomes...
Jessica B. Girault, PhD, a postdoctoral researcher at the Carolina Institute for Developmental Disabilities, is the study's lead author. UNC co-authors are Barbara D. Goldman, PhD, of UNC's Frank Porter Graham Child Development Institute, Juan C. Prieto, PhD, assistant professor, and Martin Styner, PhD, director of the Neuro Image Research and Analysis Laboratory in the department of psychiatry.
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Journal Reference
Jessica B. Girault, Brent C. Munsell, Danaële Puechmaille, Barbara D. Goldman, Juan C. Prieto, Martin Styner, John H. Gilmore. White matter connectomes at birth accurately predict cognitive abilities at age 2. NeuroImage, 2019; 192: 145 DOI:
https://doi.org/10.1016/j.neuroimage.2019.02.060
