Emily Casanova is research assistant professor of biomedical sciences at the University of South Carolina School of Medicine Greenville.

Emily Casanova
Research assistant professor
University of South Carolina
From this contributor
How the autonomic nervous system may govern anxiety in autism
The branch of the nervous system that regulates subconscious bodily processes such as breathing and digestion may play a key role in autism.

How the autonomic nervous system may govern anxiety in autism
What Ehlers-Danlos syndrome can teach us about autism
Not much is known about the connection between autism and Ehlers-Danlos syndrome, a condition that affects collagen. But preliminary work provides tantalizing clues.

What Ehlers-Danlos syndrome can teach us about autism
Evolution of autism genes hints at their fundamental roles in body
Genes associated with autism are ancient, and mutations in them have wide-ranging effects on the body, indicating their importance.

Evolution of autism genes hints at their fundamental roles in body
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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.