Biomarkers in Psychiatry
Biomarkers in Psychiatry

Author: Judith Pratt

Publisher: Springer

Published: 2019-01-05

Total Pages: 431

ISBN-13: 3319996428

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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.

Statistical Techniques for Neuroscientists
Statistical Techniques for Neuroscientists

Author: Young K. Truong

Publisher: CRC Press

Published: 2016-10-04

Total Pages: 349

ISBN-13: 1315356759

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Statistical Techniques for Neuroscientists introduces new and useful methods for data analysis involving simultaneous recording of neuron or large cluster (brain region) neuron activity. The statistical estimation and tests of hypotheses are based on the likelihood principle derived from stationary point processes and time series. Algorithms and software development are given in each chapter to reproduce the computer simulated results described therein. The book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and multimodal neuroimaging techniques. The author provides an overview of various methods being applied to specific research areas of neuroscience, emphasizing statistical principles and their software. The book includes examples and experimental data so that readers can understand the principles and master the methods. The first part of the book deals with the traditional multivariate time series analysis applied to the context of multichannel spike trains and fMRI using respectively the probability structures or likelihood associated with time-to-fire and discrete Fourier transforms (DFT) of point processes. The second part introduces a relatively new form of statistical spatiotemporal modeling for fMRI and EEG data analysis. In addition to neural scientists and statisticians, anyone wishing to employ intense computing methods to extract important features and information directly from data rather than relying heavily on models built on leading cases such as linear regression or Gaussian processes will find this book extremely helpful.

Neuroimaging in Schizophrenia
Neuroimaging in Schizophrenia

Author: Marek Kubicki

Publisher: Springer Nature

Published: 2020-02-18

Total Pages: 432

ISBN-13: 3030352064

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This comprehensive book explains the importance of imaging techniques in exploring and understanding the role of brain abnormalities in schizophrenia. The findings obtained using individual imaging modalities and their biological interpretation are reviewed in detail, and updates are provided on methodology, testable hypotheses, limitations, and new directions for research. The coverage also includes important recent applications of neuroimaging to schizophrenia, for example in relation to non-pharmacological interventions, brain development, genetics, and prediction of treatment response and outcome. Written by world renowned experts in the field, the book will be invaluable to all who wish to learn about the newest and most important developments in neuroimaging research in schizophrenia, how these developments relate to the last 30 years of research, and how they can be leveraged to bring us closer to a cure for this devastating disorder. Neuroimaging in Schizophrenia will assist clinicians in navigating what is an extremely complex field and will be a source of insight and stimulation for researchers.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014

Author: Polina Golland

Publisher: Springer

Published: 2014-08-31

Total Pages: 460

ISBN-13: 3319104438

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The three-volume set LNCS 8673, 8674, and 8675 constitutes the refereed proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, held in Boston, MA, USA, in September 2014. Based on rigorous peer reviews, the program committee carefully selected 253 revised papers from 862 submissions for presentation in three volumes. The 53 papers included in the third volume have been organized in the following topical sections: shape and population analysis; brain; diffusion MRI; and machine learning.

MRI Atlas of Human White Matter
MRI Atlas of Human White Matter

Author: Kenichi Oishi

Publisher: Academic Press

Published: 2010-11-12

Total Pages: 266

ISBN-13: 0123820820

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MRI Atlas of Human White Matter presents an atlas to the human brain on the basis of T 1-weighted imaging and diffusion tensor imaging. A general background on magnetic resonance imaging is provided, as well as the basics of diffusion tensor imaging. An overview of the principles and limitations in using this methodology in fiber tracking is included. This book describes the core white-matter structures, as well as the superficial white matter, the deep gray matter, and the cortex. It also presents a three-dimensional reconstruction and atlas of the brain white-matter tracts. The Montreal Neurological Institute coordinates, which are the most widely used, are adopted in this book as the primary coordinate system. The Talairach coordinate system is used as the secondary coordinate system. Based on magnetic resonance imaging and diffusion tensor imaging, the book offers a full segmentation of 220 white-matter and gray-matter structures with boundaries. - Visualization of brain white matter anatomy via 3D diffusion tensor imaging (DTI) contrasts and enhances relationship of anatomy to function - Full segmentation of 170+ brain regions more clearly defines structure boundaries than previous point-and-annotate anatomical labeling, and connectivity is mapped in a way not provided by traditional atlases

Neuroimaging biomarkers in Alzheimer’s disease
Neuroimaging biomarkers in Alzheimer’s disease

Author: Samuel Barrack

Publisher: iMedPub

Published: 2013-10-20

Total Pages: 134

ISBN-13: 1492274429

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In view of the growing prevalence of AD worldwide, there is an urgent need for the development of better diagnostic tools and more effective therapeutic interventions. Indeed, much work in this field has been done during last decades. As such, a major goal of current clinical research in AD is to improve early detection of disease and presymptomatic detection of neuronal dysfunction, concurrently with the development of better tools to assess disease progression in this group of disorders. All these putative correlates are commonly referred to as AD-related biomarkers. The ideal biomarker should be easy to quantify and measure, reproducible, not subject to wide variation in the general population and unaffected by co- morbid factors. For evaluation of therapies, a biomarker needs to change linearly with disease progression and closely correlate with established clinico-pathological parameters of the disease. There is growing evidence that the use of biomarkers will increase our ability to better indentify the underlying biology of AD, especially in its early stages. These biomarkers will improve the detection of the patients suitable for research studies and drug trials, and they will contribute to a better management of the disease in the clinical practice. Indeed, much work in this field has been done during last decades. The vast number of important applications, combined with the untamed diversity of already identified biomarkers, show that there is a pressing need to structure the research made on AD biomarkers into a solid, comprehensive and easy to use tool to de deployed in clinical settings. To date there are few publications compiling results on this topic. That is why when I was asked to address this task I accepted inmediately. I am happy to present you a bundle of the best articles published about biomarkers for Alzheimer’s disease in recent times.

Computational Analysis and Deep Learning for Medical Care
Computational Analysis and Deep Learning for Medical Care

Author: Amit Kumar Tyagi

Publisher: John Wiley & Sons

Published: 2021-08-24

Total Pages: 532

ISBN-13: 1119785723

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The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.