Genetic and social correlates of neurodevelopment and mental health
We analyse large-scale datasets to understand how genes and our environment shape brain development and impact mental health and neurodevelopmental conditions. We use methods from genetics, functional genomics, neuroimaging, psychology, data science, and machine learning to address these questions. We collaborate widely within and outside Cambridge.
Our ongoing research is spread across these three themes below.
Genetics of brain structure and function
We use large scale imaging data from MRI scans and link these to genetic variants to identify how genetics contribute to brain development and organisation.
Ongoing work include the genetics of brain structure and function, and their links to mental health and neurodevelopmental conditions.
Heterogeneity in neurodevelopment
We integrate multiple social, developmental, and genetic datasets to identify factors that contribute to heterogeneity in neurodevelopment, neurodevelopmental conditions, and related outcomes.
Current work focusses on understanding heterogeneity within autism, and the shared biology between autism and other neurodevelopmental and mental health conditions.
Environment and mental health
We use multiple longitudinal and cross-sectional datasets to understand how the environment contributes to various mental health conditions. We also integrate genetics to explore the combined impact of genetics and environment in mental health conditions.
Ongoing work focusses on developing methods to model the correlated and dynamic environment, and machine learning methods to predict mental health and neurodevelopmental outcomes using genetics and environmental factors.
