Forensic Medicine encompasses all areas in which medicine and law interact. This book covers diverse aspects of forensic medicine including forensic pathology, traumatology and violent death, sudden and unexpected death, clinical forensic medicine, toxicology, traffic medicine, identification, haemogenetics and medical law. A knowledge of all these subdisciplines is necessary in order to solve routine as well as more unusual cases. Taking a comprehensive approach the book m.oves beyond a focus on forensic pathology to include clinical forensic medicine and forensic toxicology. All aspects of forensic medicine are covered to meet the specialist needs of daily casework. Aspects of routine analysis and quality control are addressed in each chapter. The book provides coverage of the latest developments in forensic molecular biology, forensic toxicology, molecular pathology and immunohistochemistry. A must-have reference for every specialist in the field this book is set to become the bench-mark for the international forensic medical community.

This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science.

Machine learning (ML) and deep learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together artificial intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.

This comprehensive volume provides a balanced and easily readable account of the rise of modern sleep medicine, its history and developmental milestones. Authored by an international group of experts, the remarkable progress and fascinating evolution from rudimentary concepts of the ancient prehistoric and early classical periods to our contemporary knowledge are covered in detail. These examples and their relationship to modern therapies offer neurologists, psychiatrists, respiratory specialists, clinicians, researchers and those interested in sleep medicine an important perspective to the origins of current practice.

An overview of theoretical and computational approaches to neuroimaging.

This book represents the emerging efforts of a growing international network of researchers and practitioners to promote the development and uptake of evidence-based pedagogies in higher education, at something a level approaching large-scale impact. By offering a communication venue that attracts and enhances much needed partnerships among practitioners and researchers in pedagogical innovation, we aim to change the conversation and focus on how we work and learn together – i.e. extending the implementation and knowledge of co–design methods. In this first edition of our Research Topic on Active Learning, we highlight two (of the three) types of publications we wish to promote. First are studies aimed at understanding the pedagogical designs developed by practitioners in their own practices by bringing to bear the theoretical lenses developed and tested in the education research community. These types of studies constitute the "practice pull" that we see as a necessary counterbalance to "knowledge push" in a more productive pedagogical innovation ecosystem based on research-practitioner partnerships. Second are studies empirically examining the implementations of evidence-based designs in naturalistic settings and under naturalistic conditions. Interestingly, the teams conducting these studies are already exemplars of partnerships between researchers and practitioners who are uniquely positioned as “in-betweens” straddling the two worlds. As a result, these publications represent both the rigours of research and the pragmatism of reflective practice. In forthcoming editions, we will add to this collection a third type of publication -- design profiles. These will present practitioner-developed pedagogical designs at varying levels of abstraction to be held to scrutiny amongst practitioners, instructional designers and researchers alike. We hope by bringing these types of studies together in an open access format that we may contribute to the development of new forms of practitioner-researcher interactions that promote co-design in pedagogical innovation.

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.