Mustafa Sahin is professor of neurology at Harvard University and director of the Translational Neuroscience Center at Boston Children’s Hospital.
Mustafa Sahin
Professor
Harvard University
From this contributor
Studies of tuberous sclerosis may shed light on biology of autism
Tuberous sclerosis provides a unique opportunity to understand autism because about half of people with that single-gene condition also have autism.

Studies of tuberous sclerosis may shed light on biology of autism
Insights for autism from tuberous sclerosis complex
Studying tuberous sclerosis provides researchers with a unique opportunity to find a common pathway among the various genetic causes of autism, says neurologist Mustafa Sahin.

Insights for autism from tuberous sclerosis complex
Explore more from The Transmitter
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.