Imaging Genetics, 1st Edition

  • Adrian Dalca
  • Kayhan Batmanghelich
  • Mert Sabuncu
  • Li Shen
  • Published By:
  • ISBN-10: 0128139692
  • ISBN-13: 9780128139691
  • DDC: 576.5
  • Grade Level Range: College Freshman - College Senior
  • 182 Pages | eBook
  • Original Copyright 2018 | Published/Released May 2018
  • This publication's content originally published in print form: 2018

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This text 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.

Table of Contents

Front Cover.
Half Title Page.
The Elsevier and Miccai Society Book Series.
Title Page.
Copyright Page.
List of Contributors.
List of Figures.
1: Multisite Metaanalysis of Image-Wide Genome-Wide Associations With Morphometry.
2: Genetic Connectivity—Correlated Genetic Control of Cortical Thickness, Brain Volume, and White Matter.
3: Integration of Network-Based Biological Knowledge With White Matter Features in Preterm Infants Using the Graph-Guided Group Lasso.
4: Classifying Schizophrenia Subjects by Fusing Networks From Single-Nucleotide Polymorphisms, DNA Methylation, and Functional Magnetic Resonance Imaging Data.
5: Genetic Correlation Between Cortical Gray Matter Thickness and White Matter Connections.
6: Bootstrapped Sparse Canonical Correlation Analysis: Mining Stable Imaging and Genetic Associations With Implicit Structure Learning.
7: A Network-Based Framework for Mining High-Level Imaging Genetic Associations.
8: Bayesian Feature Selection for Ultrahigh Dimensional Imaging Genetics Data.
9: Continuous Inflation Analysis: A Threshold-Free Method to Estimate Genetic Overlap and Boost Power in Imaging Genetics.