Meng-Chuan Lai is associate professor of psychiatry at the University of Toronto in Canada.
Meng-Chuan Lai
Assistant professor
University of Toronto
From this contributor
Quashing sex bias in autism research calls for participant rainbow
Autism researchers must attend to the need for sex and gender diversity in study design as a rule rather than as an exception.
Quashing sex bias in autism research calls for participant rainbow
Brains of girls, boys may mark distinct paths to autism
Differences between the brains of men and women with autism may help explain why men are more susceptible to the condition and women appear to be protected from it.
Brains of girls, boys may mark distinct paths to autism
Mind the gender gap
Autism may be male-biased in prevalence, but our understanding of it should not be, argues Meng-Chuan Lai.
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The term ‘model organism’ should be reserved for biological systems
By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.
The term ‘model organism’ should be reserved for biological systems
By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.
New phase of BRAIN Initiative spawns basal ganglia ‘parts list’
With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.
New phase of BRAIN Initiative spawns basal ganglia ‘parts list’
With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.
Machine-learning model spots autism-linked variants that scramble splicing
The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.
Machine-learning model spots autism-linked variants that scramble splicing
The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.