Center for Population Neuroscience and Genetics (PNG)
Our Research ApproachDr. Thompson's research focuses on the development and application of semi-parametric Bayesian hierarchical and mixture models for multivariate data, with applications to diverse areas of biological psychiatry.
These applications include:
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Research Program Highlights
Large-scale Multi-modal Neuroimaging Analyses
We are developing multivariate methods to enhance the power and validity of associating brain measures with behavioral outcomes and to obtain unbiased estimates of the impact of genetics and environment on behavioral outcomes, as mediated by multi-modal neuroimaging data.
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Polygenic Inferences
We utilize the genetics as an inferential instrument for gaining insight into the molecular processes underlying brain development. We are particularly interested in a) genetic determinants on high-dimensional multivariate outcomes, b) enhancing predictions using multi-omics, and c) robust inferences across genetic ancestry background.
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National Registries and Biobanks
Global efforts linking biosamples and health registries have enabled researchers to examine the relationships between genes and diseases without the problem of ascertainment biases. We have international collaborations that allow us to apply our approaches on large-scale population data to understand the path from neural circuits to psychiatric illness.
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Scientific Background
Dr. Thompson earned his Ph.D. in Statistics from Rutgers University in 2003, with a focus on statistical methods for longitudinal data analysis. He was appointed Assistant Professor of Statistics and Psychiatry at the University of Pittsburgh in 2005, where he received a five year NIH K25 Career Development Award to develop novel methods for studying co-variation in brain function and depression. Dr. Thompson joined the UCSD Department of Psychiatry in 2008. In 2022, Dr. Thompson joined the Laureate Institute for Brain Research to form the Center for Population Neuroscience and Genetics with his longtime colleague, Dr. Chun Fan. His current work involves Bayesian semi-parametric and mixture models with applications to (i) improving effect size estimation, replication, and prediction in genome-wide association studies, (ii) predicting onset of illness from multivariate biomarker trajectories, (iii) applications of functional data analysis to functional MRI data.