ArticleProceedings. IEEE International Symposium on Biomedical Imaging2016
INTEGRATIVE BAYESIAN ANALYSIS OF NEUROIMAGING-GENETIC DATA THROUGH HIERARCHICAL DIMENSION REDUCTION.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- White matter microstructure differences in obstructive sleep apnea severity groups assessed by diffusion tensor metrics and biophysical modeling.Scientific reports · 2026Article
- Neuroimaging feature extraction using a neural network classifier for imaging genetics.BMC bioinformatics · 2023Article
- Exploring the relationship between white matter integrity, cocaine use and GAD polymorphisms using Bayesian Model Averaging.PloS one · 2021Article
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6 authors.
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Abstract
Advances in neuromedicine have emerged from endeavors to elucidate the distinct genetic factors that influence the changes in brain structure that underlie various neurological conditions. We present a framework for examining the extent to which genetic factors impact imaging phenotypes described by voxel-wise measurements organized into collections of functionally relevant regions of interest (ROIs) that span the entire brain. Statistically, the integration of neuroimaging and genetic data is challenging. Because genetic variants are expected to impact different regions of the brain, an appropriate method of inference must simultaneously account for spatial dependence and model uncertainty. Our proposed framework combines feature extraction using generalized principal component analysis to account for inherent short- and long-range structural dependencies with Bayesian model averaging to effectuate variable selection in the presence of multiple genetic variants. The methods are demonstrated on a cocaine dependence study to identify ROIs associated with genetic factors that impact diffusion parameters.
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