Salience network-based classification and prediction of symptom severity in children with autism.

ABSTRACT:

Importance

Autism spectrum disorder (ASD) affects 1 in 88 children and is characterized by a complex phenotype, including social, communicative, and sensorimotor deficits. Autism spectrum disorder has been linked with atypical connectivity across multiple brain systems, yet the nature of these differences in young children with the disorder is not well understood.

Objectives

To examine connectivity of large-scale brain networks and determine whether specific networks can distinguish children with ASD from typically developing (TD) children and predict symptom severity in children with ASD.

Design, setting, and participants

Case-control study performed at Stanford University School of Medicine of 20 children 7 to 12 years old with ASD and 20 age-, sex-, and IQ-matched

SUBMITTER: Uddin LQ 

PROVIDER: S-EPMC3951904 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature