MRI at birth can predict cognitive development at two years of age

Cognitive ability is an important predictor of mental health outcomes that is influenced by neurodevelopment. Evidence suggests that the foundational wiring of the human brain is in place by birth, and that the white matter (WM) connectome supports developing brain function. It is unknown, however, how the WM connectome at birth supports emergent cognition.

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. (Girault JB et al. White matter connectomes at birth accurately predict cognitive abilities at age 2. Neuroimage. 2019 May 15; 192: 145-155. doi: 10.1016/j.neuroimage.2019.02.060).
In this study, a deep learning model was trained using cross-validation to classify full-term infants (n = 75) as scoring above or below the median at two years age  using WM connectomes generated from diffusion weighted MR images at birth.
“This prediction could help identify children at risk for poor cognitive development shortly after birth with high accuracy,” said senior author Dr. J H. Gilmore. “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.”

Gilmore said researchers at UNC and elsewhere are working to find imaging biomarkers of risk for poor cognitive outcomes and for risk of neuropsychiatric conditions such as autism and schizophrenia. In this study, the researchers replicated the initial finding in a second sample of children who were born prematurely.

“Our study finds that the white matter network at birth is highly predictive and may be a useful imaging biomarker. The fact that we could replicate the findings in a second set of children provides strong evidence that this may be a real and generalizable finding,” he said.

doi. 10.1016/j.neuroimage.2019.02.060