Elizabeth Berry-Kravis is professor of child neurology at Rush University Medical Center in Chicago.
Elizabeth Berry-Kravis
Professor
Rush University Medical Center
From this contributor
Analysis offers new hope for failed fragile X drug
Eye tracking shows that mavoglurant, a once-abandoned experimental drug for fragile X syndrome, enters the brain and boosts social interest, says Elizabeth Berry-Kravis.

Analysis offers new hope for failed fragile X drug
Questions for Elizabeth Berry-Kravis: Dodging mouse traps
A mouse model of fragile X syndrome lacks a key feature of the condition, prompting researchers to look for other ways to test treatments.

Questions for Elizabeth Berry-Kravis: Dodging mouse traps
Questions for Elizabeth Berry-Kravis: Measuring drug effects
Drugs designed to treat fragile X syndrome have yet to show substantial benefits in people. But rather than abandon them, child neurologist Elizabeth Berry-Kravis suggests a new way to measure their effectiveness.

Questions for Elizabeth Berry-Kravis: Measuring drug effects
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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.