Machine learning identifies personalized brain networks in children

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 published today in the journal Neuron, 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 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.