COLUMBUS, Ohio (WCMH) — A study that analyzed functional MRI scans of 267 newborns showed that five brain networks in newborns resemble those found in adults, with two not present yet.
The study from researchers at Ohio State University, to be published in the journal NeuroImage in June, showed that newborns — most less than a week old — have five networks in their brains that resemble those of adults: the visual, default, sensorimotor, ventral attention and high-level vision networks.
Two networks not present in newborns were the control and limbic networks. These are both involved with higher-level functions, said Zeynep Saygin, senior author of the study and an Ohio State assistant professor of psychology.
The control network allows adults to make plans to meet goals. The limbic network is involved in emotional regulation, the study authors said.
Researchers found individual variability in networks in newborns, which may have implications for how genetics affects behavior in adults.
“Our study shows variability in the brain at birth that may be related to some of the behavioral differences we see in adults,” Saygin said.
The researchers also examined individual differences in the brain networks of the newborns studied. Results showed that the ventral attention network showed the most variability in the newborns. This is the network involved in directing attention to important stimuli encountered in the world, especially something that may be unexpected.
Fiona Molloy, a psychology graduate student at Ohio State, led the study.
“We see individual differences in network organization as early as birth, and it could be interesting to see if these differences predict behavior or risk of psychological disorders later in life,” Molloy said in the news release.
Future research will examine how these networks develop over time to get a better understanding of the role of genetic programming and experience in producing variability in these networks.
“We want to further understand the developmental trajectory of these networks to learn how genes and experience relate to future behavior and outcomes,” Saygin said.