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. As large-scale imaging genetics datasets are becoming available, their analysis poses unprecedented methodological and computational challenges. 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 of research. Contains an Introduction describing how the field has evolved to the present, together with perspectives on its future direction, and the future research challenges to be tackled - an ideal starting point for a researcher new to the fieldDescribes novel application domains and analytic methods that represent the state-of-the-art in the burgeoning field of imaging geneticsIntroduces a novel large-scale analytic framework that involves multi-site image-wide genome-wide associationsA title within the prestigious MICCAI Society Book series INDICE: 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
- ISBN: 978-0-12-813968-4
- Editorial: Academic Press
- Encuadernacion: Rústica
- Páginas: 195
- Fecha Publicación: 01/10/2017
- Nº Volúmenes: 1
- Idioma: Inglés