Tony Charman
Chair of clinical child psychology
King's College London
From this contributor
Building an autism research registry: Q&A with Tony Charman
A purpose-built database of participants who have shared genomic and behavioral data could give clinical trials a boost, Charman says.
Building an autism research registry: Q&A with Tony Charman
Your questions about the Lancet Commission and ‘profound autism,’ answered
Tony Charman and Catherine Lord answer questions from Spectrum’s webinar on the Lancet Commission’s recommendations for autism research.
Your questions about the Lancet Commission and ‘profound autism,’ answered
Separate thinking skills underlie autism, attention deficit
Theory of mind difficulties are likely to be more central to autism than to attention deficit hyperactive disorder, whereas executive function problems are more often associated with the latter.
Separate thinking skills underlie autism, attention deficit
Tony Charman: Longitudinal studies for autism research
Clinicians and autism researchers should learn the early signs of autism and take into account an individual’s developmental trajectory, says Tony Charman.
Tony Charman: Longitudinal studies for autism research
Explore more from The Transmitter
Leucovorin saga, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 15 June.
Leucovorin saga, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 15 June.
Models at the speed of thought: How AI coding is reshaping theoretical neuroscience
Agentic coding makes it possible to specify a neuroscience model in hours instead of months. Six neuroscientists weigh in on what that tectonic change may bring to the field.
Models at the speed of thought: How AI coding is reshaping theoretical neuroscience
Agentic coding makes it possible to specify a neuroscience model in hours instead of months. Six neuroscientists weigh in on what that tectonic change may bring to the field.
Writing science that humans and machines can read
Large language models are now routinely used to search, summarize and synthesize the literature at scales impossible for any individual researcher—yet scientific publishing has not adapted to that reality.
Writing science that humans and machines can read
Large language models are now routinely used to search, summarize and synthesize the literature at scales impossible for any individual researcher—yet scientific publishing has not adapted to that reality.