Imaging Genetics

ISBN-13: 9780128139684 / Angielski / Miękka / 2017 / 182 str.

Adrian Dalca; Nematollah K. Batmanghelich; Mert Sabuncu

Opis

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

Spis treści

1. Genetic correlation between cortical gray matter thickness and white matter connections2. BoSCCA: Mining Stable Imaging and Genetic Associations with Implicit Structure Learning3. Multi-site meta-analysis of image-wide genome-wide associations with morphometry4. Network-based analysis for subcortical imaging measures and genetics association5. Identification of genes in lipid metabolism associated with white matter integrity in preterm infants using the graph-guided group lasso6. Genetic connectivity: correlated genetic control of cortical thickness, brain volume and white-matter7. Continuous inflation analysis: a threshold-free method to estimate genetic overlap and boost power in imaging genetics8. Bayesian Feature Selection for Ultra-high Dimensional Imaging Genetics Data9. Classifying Schizophrenia subjects by Fusing Networks from SNPs, DNA methylation and fMRI data

O autorze

Adrian V. Dalca is a postdoctoral fellow at Massachusetts General Hospital and Harvard Medical School. He obtained his PhD from the Massachusetts Institute of Technology in the EECS department. He is interested in mathematical models and machine learning for medical image analysis, with a focus on characterizing genetic and clinical effects on imaging phenotypes. He is also interested and active in healthcare entrepreneurship and translation of algorithms to the clinic. Kayhan Batmanghelich is an Assistant Professor of Department of Biomedical Informatics and Intelligent Systems Program at the University of Pittsburgh and an adjunct faculty in the Machine Learning Department at the Carnegie Mellon University. His research is at the intersection of medical vision, machine learning, and bioinformatics. He develops algorithms to analyze and understand medical image along with genetic data and other electrical health records such as the clinical report. He is interested in method development as well as translational clinical problems. Mert Sabuncu is an Assistant Professor in Electrical and Computer Engineering, with a secondary appointment in Biomedical Engineering, Cornell University. His research interests are in biomedical data analysis, in particular imaging data, and with an application emphasis on neuroscience and neurology. He uses tools from signal/image processing, probabilistic modeling, statistical inference, computer vision, computational geometry, graph theory, and machine learning to develop algorithms that allow learning from large-scale biomedical data. Li Shen received a B.S. degree from Xi'an Jiao Tong University, an M.S. degree from Shanghai Jiao Tong University, and a Ph.D. degree from Dartmouth College, all in Computer Science. He is an Associate Professor of Radiology and Imaging Sciences at Indiana University School of Medicine. His research interests include medical image computing, bioinformatics, machine learning, network science, brain imaging genomics, and big data science in biomedicine.