Cheryl Platzman Weinstock is an award–winning journalist who reports about health and science research and its impact on society. Her investigative pieces have brought attention to mental health, medical ethics issues and the medical research gender gap. She also writes for the Metro desk of The New York Times.
Cheryl Platzman Weinstock
From this contributor
The deep emotional ties between depression and autism
Autistic people are four times as likely to experience depression over the course of their lives as their neurotypical peers. Yet researchers know little about why, or how best to help.
The deep emotional ties between depression and autism
By
Cheryl Platzman Weinstock
31 July 2019 | 14 min read
The hidden danger of suicide in autism
Many people with autism entertain thoughts of suicide and yet show few obvious signs of their distress. Some scientists are identifying risks — and solutions — unique to autistic individuals.
The hidden danger of suicide in autism
By
Cheryl Platzman Weinstock
8 August 2018 | 16 min read
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Sharing Africa’s brain data: Q&A with Amadi Ihunwo
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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.
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Lauren Schenkman
20 May 2025 | 6 min read
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.
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Jill Adams
20 May 2025 | 2 min read
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.
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Alona Fyshe
19 May 2025 | 7 min read