Machine learning streamlines neuroimaging data analysis
The method could boost reproducibility across brain imaging studies of autism.
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.
Infusions of the hormone oxytocin may make mice that model autism more social by normalizing their brain activity patterns.
Four subtypes lend new support to the idea that there isn’t a single ‘hallmark’ type of brain connectivity in people with autism.
Brainhack conferences offer talks and hands-on tutorials, and unite small groups of interdisciplinary researchers to work on open-source neuroscience projects.
Reports of flaws in imaging research are signs of a maturing field, experts say, not a devastating blow to its results.
Autistic children with sensory issues show more intense physiological reactions to unpleasant sounds and other sensations than their non-autistic peers do, a new study shows.