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Tackling the Inverse Problem for Non-Autonomous Systems
This thesis presents a new method for following evolving interactions between coupled oscillatory systems of the kind that abound in nature. Examples range from the subcellular level, to ecosystems, through climate dynamics, to the movements of planets and stars. Such systems mutually interact, adjusting their internal clocks, and may correspondingly move between synchronized and non-synchronized states. The thesis describes a way of using Bayesian inference to exploit the presence of random fluctuations, thus analyzing these processes in unprecedented detail. It first develops the basic theory of interacting oscillators whose frequencies are non-constant, and then applies it to the human heart and lungs as an example. Their coupling function can be used to follow with great precision the transitions into and out of synchronization. The method described has the potential to illuminate the ageing process as well as to improve diagnostics in cardiology, anesthesiology and neuroscience, and yields insights into a wide diversity of natural processes.
Alzheimer’s Disease: New Beginnings
Alzheimer’s Disease: New Beginnings focuses on the future promise for therapeutic breakthroughs in light of notable clinical trial failures. The volume editors used a combination of scientometric evaluations to determine the most promising new approaches as well as soliciting insights from leaders in each of the major areas of Alzheimer’s disease research. By combining these two approaches, they recruited authors from the entire outlook spectrum, from those who feel an elusive breakthrough might still be a few, well-placed tweaks away to those who feel that they are launching entirely new investigative paradigms. These scholars present an open-eyed path forward. Now is the most exciting period in this field as old dogmas make way for new insights, from new approaches to clinical trials, improved biomarker-based diagnostics, population-based studies, prevention, metabolism, to further refinement of the role of inflammation, genetics, tau, and amyloid-ß.
Predictive Imagable Biomarkers for Brain Disorders
Recent research on Neurodegenerative disorders such as Alzheimer's Disease, Parkinson's Disease, etc. and Neuropsychiatric disorders such as Schizophrenia, has shown strong evidence that altered brain tissue structure, physiology, and connectivity reflect the extent of severity of behavioral and physical abnormalities. With the advancement of high field strength MR technologies like MRS (Magnetic Resonance Spectroscopy), fMRI (functional Magnetic Resonance Imaging) and DTI (Diffusion Tensor Imaging), it has become possible to non-invasively measure these changes brain microenvironment in terms of levels of antioxidants; neurotransmitters; regional activity, susceptibility and connectivity du...
Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications
Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging,...
Handbook of Medical Image Computing and Computer Assisted Intervention
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. - Presents the key research challenges in medical image computing and computer-assisted intervention - Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society - Contains state-of-the-art technical approaches to key challenges - Demonstrates proven algorithms for a whole range of essential medical imaging applications - Includes source codes for use in a plug-and-play manner - Embraces future directions in the fields of medical image computing and computer-assisted intervention
