Imaging Genetics

1. Genetic correlation between cortical gray matter thickness and white matter connections
2. BoSCCA: Mining Stable Imaging and Genetic Associations with Implicit Structure Learning
3. Multi-site meta-analysis of image-wide genome-wide associations with morphometry
4. Network-based analysis for subcortical imaging measures and genetics association
5. Identification of genes in lipid metabolism associated with white matter integrity in preterm infants using the graph-guided group lasso
6. Genetic connectivity: correlated genetic control of cortical thickness, brain volume and white-matter
7. Continuous inflation analysis: a threshold-free method to estimate genetic overlap and boost power in imaging genetics
8. Bayesian Feature Selection for Ultra-high Dimensional Imaging Genetics Data
9. Classifying Schizophrenia subjects by Fusing Networks from SNPs, DNA methylation and fMRI data

Imaging Genetics presents the latest research in imaging genetics methodology for discovering new associations between imaging and genetic variables, providing an overview of the state-of the-art in the field. Edited and written by leading researchers, this book is a beneficial reference for students and researchers, both new and experienced, in this growing area. The field of imaging genetics studies the relationships between DNA variation and measurements derived from anatomical or functional imaging data, often in the context of a disorder. While traditional genetic analyses rely on classical phenotypes like clinical symptoms, imaging genetics can offer richer insights into underlying, complex biological mechanisms.

• Contains an introduction describing how the field has evolved to the present, together with perspectives on its future direction and challenges
• Describes novel application domains and analytic methods that represent the state-of-the-art in the burgeoning field of imaging genetics
• Introduces a novel, large-scale analytic framework that involves multi-site, image-wide, genome-wide associations

Researchers and graduate students in medical imaging and computer vision