Brain Connectivity in Autism


Book Description

The brain's ability to process information crucially relies on connectivity. Understanding how the brain processes complex information and how such abilities are disrupted in individuals with neuropsychological disorders will require an improved understanding of brain connectivity. Autism is an intriguingly complex neurodevelopmental disorder with multidimensional symptoms and cognitive characteristics. A biological origin for autism spectrum disorders (ASD) had been proposed even in the earliest published accounts (Kanner, 1943; Asperger, 1944). Despite decades of research, a focal neurobiological marker for autism has been elusive. Nevertheless, disruptions in interregional and functional and anatomical connectivity have been a hallmark of neural functioning in ASD. Theoretical accounts of connectivity perceive ASD as a cognitive and neurobiological disorder associated with altered functioning of integrative circuitry. Neuroimaging studies have reported disruptions in functional connectivity (synchronization of activated brain areas) during cognitive tasks and during task-free resting states. While these insights are valuable, they do not address the time-lagged causality and directionality of such correlations. Despite the general promise of the connectivity account of ASD, inconsistencies and methodological differences among studies call for more thorough investigations. A comprehensive neurological account of ASD should incorporate functional, effective, and anatomical connectivity measures and test the diagnostic utility of such measures. In addition, questions pertaining to how cognitive and behavioral intervention can target connection abnormalities in ASD should be addressed. This research topic of the Frontiers in Human Neuroscience addresses “Brain Connectivity in Autism” primarily from cognitive neuroscience and neuroimaging perspectives.




Machine Learning in Radiation Oncology


Book Description

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.




Applied Pattern Recognition


Book Description

A sharp increase in the computing power of modern computers has triggered the development of powerful algorithms that can analyze complex patterns in large amounts of data within a short time period. Consequently, it has become possible to apply pattern recognition techniques to new tasks. The main goal of this book is to cover some of the latest application domains of pattern recognition while presenting novel techniques that have been developed or customized in those domains.




2021 6th International Conference for Convergence in Technology (I2CT)


Book Description

Microwave Wireless Networking Computational Intelligence Advanced Computing Electronics and Interdisciplinary Data Communication and Networking Renewable and Sustainable Energy Power Engineering and Control System Signal and Image Processing Communication System Biomedical Engineering Design, Materials and Manufacturing Fleet Technologies




Magnetic Resonance Brain Imaging


Book Description

This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain. The data processing pipelines described rely on R. The book is intended for readers from two communities: Statisticians who are interested in neuroimaging and looking for an introduction to the acquired data and typical scientific problems in the field; and neuroimaging students wanting to learn about the statistical modeling and analysis of MRI data. Offering a practical introduction to the field, the book focuses on those problems in data analysis for which implementations within R are available. It also includes fully worked examples and as such serves as a tutorial on MRI analysis with R, from which the readers can derive their own data processing scripts. The book starts with a short introduction to MRI and then examines the process of reading and writing common neuroimaging data formats to and from the R session. The main chapters cover three common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, and Multi-Parameter Mapping. The book concludes with extended appendices providing details of the non-parametric statistics used and the resources for R and MRI data.The book also addresses the issues of reproducibility and topics like data organization and description, as well as open data and open science. It relies solely on a dynamic report generation with knitr and uses neuroimaging data publicly available in data repositories. The PDF was created executing the R code in the chunks and then running LaTeX, which means that almost all figures, numbers, and results were generated while producing the PDF from the sources.




Biomarkers in Psychiatry


Book Description

This volume addresses one of the Holy Grails in Psychiatry, namely the evidence for and potential to adopt ‘Biomarkers’ for prevention, diagnosis, and treatment responses in mental health conditions. It meshes together state of the art research from international renowned pre-clinical and clinical scientists to illustrate how the fields of anxiety disorders, depression, psychotic disorders, and autism spectrum disorder have advanced in recent years.




Emerging Technologies for Healthcare


Book Description

“Emerging Technologies for Healthcare” begins with an IoT-based solution for the automated healthcare sector which is enhanced to provide solutions with advanced deep learning techniques. The book provides feasible solutions through various machine learning approaches and applies them to disease analysis and prediction. An example of this is employing a three-dimensional matrix approach for treating chronic kidney disease, the diagnosis and prognostication of acquired demyelinating syndrome (ADS) and autism spectrum disorder, and the detection of pneumonia. In addition, it provides healthcare solutions for post COVID-19 outbreaks through various suitable approaches, Moreover, a detailed detection mechanism is discussed which is used to devise solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions. This book not only covers theoretical approaches and algorithms, but also contains the sequence of steps used to analyze problems with data, processes, reports, and optimization techniques. It will serve as a single source for solving various problems via machine learning algorithms.




Deep Learning in Medical Image Analysis


Book Description

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.




Development and Brain Systems in Autism


Book Description

The volume covers several perspectives on autism which bring together the most recent scientific views of the nature of this disorder. A number of themes organize major developments and emerging areas in autism. The book is essential for reseachers and practitioners who require a state-of-the-art resource on autism.




Artificial Intelligence in Medical Imaging


Book Description

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.