Jeanne Erdmann is an award-winning health and science writer based in Wentzville, Mo. A member of Association of
Health Care Journalists board of directors, she is the chair of the organization’s Freelance Committee. Her work has appeared in Discover, Women’s Health, Aeon, Slate, The Washington Post, Nature, Nature Medicine and other publications. You can follow her at @jeanne_erdmann.
Jeanne Erdmann
From this contributor
Analysis pins down prevalence of mental health conditions in autism
Eight mental health conditions occur unusually often in autistic people, a new analysis suggests.

Analysis pins down prevalence of mental health conditions in autism
Drug screen reveals potential treatments for Rett syndrome
An experimental leukemia drug and a chemical in black pepper ease breathing and movement problems in a mouse model of Rett syndrome.

Drug screen reveals potential treatments for Rett syndrome
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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.