Joseph Piven is Thomas E. Castelloe Distinguished Professor of Psychiatry and director of the Carolina Institute for Developmental Disabilities at the University of North Carolina at Chapel Hill. He is also director of the NIH Autism Center of Excellence Network’s Infant Brain Imaging Study (IBIS). He served on the American Psychiatric Association committee that wrote the new definition of autism spectrum disorder for the DSM-5.
Joseph Piven
Professor
University of North Carolina at Chapel Hill
From this contributor
Correcting the record: Leo Kanner and the broad autism phenotype
The specter of the ‘refrigerator mother’ theory continues to haunt the history of autism. New information puts Kanner’s observations of parents into context.

Correcting the record: Leo Kanner and the broad autism phenotype
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Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.

Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.

Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.

The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.