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Machine learning is helping Penn Medicine researchers identify the size and shape of brain networks in individual children, which may be useful for understanding psychiatric disorders. In a new study publishedin the journalNeuron,a multidisciplinary team 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.

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 liningup activation patternsthe software of the brainto 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. However, multiple recent studies in adults have shown this is not the case for more complex brain systems responsible for executive functiona set of mental processes which includes self-control and attention. In these systems, the functional networks do not always line up with the brains 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.

The exciting part of this work is that we are now able to identify the spatial layout of these functional networks in individual kids, rather than looking at everyone using the same one size fits all approach, says senior authorTheodore D. Satterthwaite, an assistant professor of psychiatry in the Perelman School of Medicine. Like adults, we found that functional neuroanatomy varies quite a lot among different kidseach child has a unique pattern. Also like adults, the networks that vary the most between kids are the same executive networks responsible for regulating the sorts of behaviors that can often land adolescents in hot water, like risk taking and impulsivity.

Read more at Penn Medicine News.

The rest is here:
Machine learning identifies personalized brain networks in children - Penn: Office of University Communications

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