Congrats to Zaixu Cui, PhD!!! Zaixu's first paper in the lab, published in the journal Neuron, showed how brain networks unique to each child can predict cognition. The study—which used machine learning techniques to analyze the functional magnetic resonance imaging (fMRI) scans of nearly 700 children, adolescents, and young adults—is the first to show that functional neuroanatomy can vary greatly among kids, and is refined during development. These personalized networks were used to predict cognitive abilities, and may be useful for understanding psychiatric disorders.
The human brain has a pattern of folds and ridges on its surface that provide physical landmarks for finding brain areas. The functional networks that govern cognition have long been studied in humans by lining up activation patterns—the software of the brain—to the hardware of these physical landmarks. However, this process assumes that the functions of the brain are located on the same landmarks in each person. This works well for many simple brain systems, for example, the motor system controlling movement is usually right next to the same specific fold in each person. However, multiple recent studies in adults have shown this is not the case for more complex brain systems responsible for executive function—a set of mental processes which includes self-control and attention. In these systems, the functional networks do not always line up with the brain’s physical landmarks of folds and ridges. Instead, each adult has their own specific layout. Until now, it was unknown how such person-specific networks might change as kids grow up, or relate to executive function.
To study how functional networks develop in children and supports executive function, the team analyzed a large sample of adolescents and young adults (693 participants, ages 8 to 23). Machine learning techniques developed by the laboratory of collaborator Yong Fan
allowed the team to map 17 functional networks in individual children, rather than relying on the average location of these networks.
The researchers then examined how these functional networks evolved over adolescence, and were related to performance on a battery of cognitive tests. The team found that the functional neuroanatomy of these networks was refined with age, and allowed the researchers to predict how old a child with a high degree of accuracy. “The spatial layout of these networks predicted how good kids were at executive tasks,” explained Zaixu. “Kids who have more ‘real estate’ on their cortex devoted to networks responsible for executive function in fact performed better on these complex tasks.” In contrast, youth with lower executive function had less of their cortex devoted to these executive networks. Taken together, these results offer a new account of developmental plasticity and diversity.
See the full Penn Medicine press release here: https://www.pennmedicine.org/news/news-releases/2020/february/machine-learning-identifies-personalized-brain-networks-in-children Additional coverage here: https://www.inverse.com/innovation/not-all-brains-are-same
