The results highlight the importance of subgrouping study participants based on their underlying genetics, the researchers say.

The machine-learning approach could help identify how brain structure differs between autistic and non-autistic infants, the researchers say.

A well-studied brain response to sound appears earlier than usual in young children with autism.

Many brain regions develop differently between people with 22q11.2 duplications and deletions, and those trajectories also vary with a person’s diagnosis.

The method could boost reproducibility across brain imaging studies of autism.

Regions of the brain’s fear center expand in autistic children and teenagers with anxiety, but not in their autistic or non-autistic peers without anxiety.

The expression levels of certain genes that track with brain activity are different in autistic people than in their non-autistic peers.

People who have ‘profound autism’ — those with severe intellectual disability, limited communication abilities or both — tend to be excluded from research. The Autism Science Foundation seeks to change that.

The growth differences vary between autistic boys and girls and are most apparent among children with prominent social difficulties.

Statistical modeling and machine learning helps blunt the bias in brain imaging studies that exclude young autistic children and those with prominent traits, a new study finds.