A new measure shows how greatly movement influences associations between traits and brain activity, revealing abundant false positives and false negatives.

Using imaging methods to sort mouse models of autism may help identify subtypes of autistic people with similar underlying biology.

Pagani used mouse models to connect autism etiologies to brain connectivity alterations and then found similar alterations in people with idiopathic forms of the condition.

Collecting brain scans from thousands of people can be challenging in autism research; data-sharing and collaborative efforts can help drive results that stand up to statistical scrutiny.

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

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

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

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.

Models trained on datasets that lack racial and ethnic diversity perform less accurately on brain scans from Black Americans than their white counterparts.

An overreliance on small studies with limited reproducibility has slowed the advancement of neuroimaging, a new analysis suggests.