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 in Neuron, a multidisciplinary team showed how brain networks unique to each child can predict cognition. The study is the first to show that functional neuroanatomy can vary greatly among kids, and is refined during development.

Read more

In Some Children with Autism, “Social” and “Visual” Neural Circuits Don’t Quite Connect

Researchers combined eye gaze research with brain scans to discover that in a common subtype of autism, in which ASD toddlers prefer images of geometric shapes over those of children playing, brain areas responsible for vision and attention are not controlled by social brain networks, and so social stimuli are ignored.

Read more