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.

The field of neuroimaging genetics has grown exponentially over the past decade. To date there are more than 10,000 published papers involving MRI, PET, MEG and genetics. Neuroimaging Genetics: Principles and Practices is the comprehensive volume edited by Drs. Bigos, Hariri, and Weinberger and co-authored by the preeminent scholars in the field. This text reviews the basic principles of neuroimaging techniques and their application to neuroimaging genetics. The work presented in this volume elaborates on the explosive interest from diverse research areas in psychiatry and neurology in the use of imaging genetics as a unique tool to establish and identify mechanisms of risk, establish biological significance, and extend statistical evidence of genetic associations. Examples throughout highlight the application of imaging genetics to understand neurochemical systems and pathways, explore relationships between genetics and the structural and functional connectivity in human brain, and provide insight into mechanisms of risk for psychiatric and neurologic illness.

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. - Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems - Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics - Features self-contained chapters with a thorough literature review - Assesses the development of future machine learning techniques and the further application of existing techniques

A comprehensive, multidisciplinary review, Neural Plasticity and Memory: From Genes to Brain Imaging provides an in-depth, up-to-date analysis of the study of the neurobiology of memory. Leading specialists share their scientific experience in the field, covering a wide range of topics where molecular, genetic, behavioral, and brain imaging techniq

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

This foundational work comprehensively examines the current state of the genetics, genomics and brain circuitry of psychiatric and neurological disorders. It consolidates discoveries of specific genes and genomic regions associated with these conditions, the genetic and anatomic architecture of these syndromes, and addresses how recent advances in genomics are leading to a reappraisal of the biology underlying clinical neuroscience. In doing so, it critically examines the promise and limitations of these discoveries toward treatment, and to the interdisciplinary nature of understanding brain and behavior. Coverage includes new discoveries regarding autism, epilepsy, intellectual disability, dementias, movement disorders, language impairment, disorders of attention, schizophrenia, and bipolar disorder. Genomics, Circuits, and Pathways in Clinical Neuropsychiatry focuses on key concepts, challenges, findings, and methods in genetics, genomics, molecular pathways, brain circuitry, and related neurobiology of neurologic and psychiatric disorders. - Provides interdisciplinary appeal in psychiatry, neurology, neuroscience, and genetics - Identifies key concepts, methods, and findings - Includes coverage of multiple disorders from autism to schizophrenia - Reviews specific genes associated with disorders - Discusses the genetic architecture of these syndromes - Explains how recent findings are influencing the understanding of biology - Clarifies the promise of these findings for future treatment

This volume explores the revolutionary fMRI field from basic principles to state-of-the-art research. It covers a broad spectrum of topics, including the history of fMRI's development using endogenous MR blood contrast, neurovascular coupling, pulse sequences for fMRI, quantitative fMRI; fMRI of the visual system, auditory cortex, and sensorimotor system; genetic imaging using fMRI, multimodal neuroimaging, brain bioenergetics and function and molecular-level fMRI. Comprehensive and intuitively structured, this book engages the reader with a first-person account of the development and history of the fMRI field by the authors. The subsequent sections examine the physiological basis of fMRI, the basic principles of fMRI and its applications and the latest advances of the technology, ending with a discussion of fMRI’s future. fMRI: From Nuclear Spins to Brain Function, co-edited by leading and renowned fMRI researchers Kamil Ugurbil, Kamil Uludag and Lawrence Berliner, is an ideal resource for clinicians and researchers in the fields of neuroscience, psychology and MRI physics.