Download or read book Statistical Inference for Biophysical Image and Network Data written by Jacob Mark Hofman and published by . This book was released on 2008 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Computing in Nuclear Imaging written by Arkadiusz Sitek and published by CRC Press. This book was released on 2014-12-17 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is highly focused on computational aspects of Bayesian data analysis of photon-limited data acquired in tomographic measurements in nuclear imaging. Basic Bayesian statistical concepts, elements of Bayesian decision theory, and counting statistics are discussed in the first chapters. Monte Carlo methods and Markov chains in posterior analysis are discussed next along with an introduction to nuclear imaging and applications. The final chapter includes illustrative examples of statistical computing based on Poisson-multinomial statistics. Examples include calculation of Bayes factors and risks, and Bayesian decision making and hypothesis testing.

Download or read book Statistical Parametric Mapping The Analysis of Functional Brain Images written by William D. Penny and published by Elsevier. This book was released on 2011-04-28 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. An essential reference and companion for users of the SPM software Provides a complete description of the concepts and procedures entailed by the analysis of brain images Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data Stands as a compendium of all the advances in neuroimaging data analysis over the past decade Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes Structured treatment of data analysis issues that links different modalities and models Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible

Download or read book Bioimage Data Analysis Workflows written by Kota Miura and published by Springer Nature. This book was released on 2019-10-17 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows. The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.

Download or read book Big Data in Omics and Imaging written by Momiao Xiong and published by CRC Press. This book was released on 2018-06-14 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.

Download or read book Biomedical Image Analysis written by Aly A. Farag and published by Cambridge University Press. This book was released on 2014-10-30 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ideal for classroom use and self-study, this book explains the implementation of the most effective modern methods in image analysis, covering segmentation, registration and visualisation, and focusing on the key theories, algorithms and applications that have emerged from recent progress in computer vision, imaging and computational biomedical science. Structured around five core building blocks - signals, systems, image formation and modality; stochastic models; computational geometry; level set methods; and tools and CAD models - it provides a solid overview of the field. Mathematical and statistical topics are presented in a straightforward manner, enabling the reader to gain a deep understanding of the subject without becoming entangled in mathematical complexities. Theory is connected to practical examples in x-ray, ultrasound, nuclear medicine, MRI and CT imaging, removing the abstract nature of the models and assisting reader understanding.

Download or read book Statistical Analysis of FMRI Data written by F. Gregory Ashby and published by MIT Press. This book was released on 2011 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of statistical methods for analyzing data from fMRI experiments.

Download or read book Nanoscale Photonic Imaging written by Tim Salditt and published by Springer Nature. This book was released on 2020-06-09 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book, edited and authored by a team of world-leading researchers, provides a broad overview of advanced photonic methods for nanoscale visualization, as well as describing a range of fascinating in-depth studies. Introductory chapters cover the most relevant physics and basic methods that young researchers need to master in order to work effectively in the field of nanoscale photonic imaging, from physical first principles, to instrumentation, to mathematical foundations of imaging and data analysis. Subsequent chapters demonstrate how these cutting edge methods are applied to a variety of systems, including complex fluids and biomolecular systems, for visualizing their structure and dynamics, in space and on timescales extending over many orders of magnitude down to the femtosecond range. Progress in nanoscale photonic imaging in Göttingen has been the sum total of more than a decade of work by a wide range of scientists and mathematicians across disciplines, working together in a vibrant collaboration of a kind rarely matched. This volume presents the highlights of their research achievements and serves as a record of the unique and remarkable constellation of contributors, as well as looking ahead at the future prospects in this field. It will serve not only as a useful reference for experienced researchers but also as a valuable point of entry for newcomers.

Download or read book Bioimage Data Analysis Workflows Advanced Components and Methods written by Kota Miura and published by Springer. This book was released on 2022-03-09 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access textbook aims at providing detailed explanations on how to design and construct image analysis workflows to successfully conduct bioimage analysis. Addressing the main challenges in image data analysis, where acquisition by powerful imaging devices results in very large amounts of collected image data, the book discusses techniques relying on batch and GPU programming, as well as on powerful deep learning-based algorithms. In addition, downstream data processing techniques are introduced, such as Python libraries for data organization, plotting, and visualizations. Finally, by studying the way individual unique ideas are implemented in the workflows, readers are carefully guided through how the parameters driving biological systems are revealed by analyzing image data. These studies include segmentation of plant tissue epidermis, analysis of the spatial pattern of the eye development in fruit flies, and the analysis of collective cell migration dynamics. The presented content extends the Bioimage Data Analysis Workflows textbook (Miura, Sladoje, 2020), published in this same series, with new contributions and advanced material, while preserving the well-appreciated pedagogical approach adopted and promoted during the training schools for bioimage analysis organized within NEUBIAS – the Network of European Bioimage Analysts. This textbook is intended for advanced students in various fields of the life sciences and biomedicine, as well as staff scientists and faculty members who conduct regular quantitative analyses of microscopy images.

Download or read book Statistical Analysis of Networks and Biophysical Systems of Complex Architecture written by Olga Valba and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex organization is found in many biological systems. For example, biopolymers could possess very hierarchic structure, which provides their functional peculiarity. Understating such, complex organization allows describing biological phenomena and predicting molecule functions. Besides, we can try to characterize the specific phenomenon by some probabilistic quantities (variances, means, etc), assuming the primary biopolymer structure to be randomly formed according to some statistical distribution. Such a formulation is oriented toward evolutionary problems.Artificially constructed biological network is another common object of statistical physics with rich functional properties. A behavior of cells is a consequence of complex interactions between its numerous components, such as DNA, RNA, proteins and small molecules. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond and to adapt to changing environment. Recent theoretical advances allow us to describe cellular network structure using graph concepts to reveal the principal organizational features shared with numerous non-biological networks.The aim of this thesis is to develop bunch of methods for studying statistical and dynamic objects of complex architecture and, in particular, scale-free structures, which have no characteristic spatial and/or time scale. For such systems, the use of standard mathematical methods, relying on the average behavior of the whole system, is often incorrect or useless, while a detailed many-body description is almost hopeless because of the combinatorial complexity of the problem. Here we focus on two problems.The first part addresses to statistical analysis of random biopolymers. Apart from the evolutionary context, our studies cover more general problems of planar topology appeared in description of various systems, ranging from gauge theory to biophysics. We investigate analytically and numerically a phase transition of a generic planar matching problem, from the regime, where almost all the vertices are paired, to the situation, where a finite fraction of them remains unmatched.The second part of this work focus on statistical properties of networks. We demonstrate the possibility to define co-expression gene clusters within a network context from their specific motif distribution signatures. We also show how a method based on the shortest path function (SPF) can be applied to gene interactions sub-networks of co-expression gene clusters, to efficiently predict novel regulatory transcription factors (TFs). The biological significance of this method by applying it on groups of genes with a shared regulatory locus, found by genetic genomics, is presented. Finally, we discuss formation of stable patters of motifs in networks under selective evolution in context of creation of islands of "superfamilies".

Download or read book Computational and Statistical Approaches to Genomics written by Wei Zhang and published by Springer Science & Business Media. This book was released on 2002 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational and Statistical Genomics aims to help researchers deal with current genomic challenges. Topics covered include: overviews of the role of supercomputers in genomics research, the existing challenges and directions in image processing for microarray technology, and web-based tools for microarray data analysis; approaches to the global modeling and analysis of gene regulatory networks and transcriptional control, using methods, theories, and tools from signal processing, machine learning, information theory, and control theory; state-of-the-art tools in Boolean function theory, time-frequency analysis, pattern recognition, and unsupervised learning, applied to cancer classification, identification of biologically active sites, and visualization of gene expression data; crucial issues associated with statistical analysis of microarray data, statistics and stochastic analysis of gene expression levels in a single cell, statistically sound design of microarray studies and experiments; and biological and medical implications of genomics research.

Download or read book The Neurology of Consciousness written by Steven Laureys and published by Academic Press. This book was released on 2015-08-12 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of The Neurology of Consciousness is a comprehensive update of this ground-breaking work on human consciousness, the first book in this area to summarize the neuroanatomical and functional underpinnings of consciousness by emphasizing a lesional approach offered by the study of neurological patients. Since the publication of the first edition in 2009, new methodologies have made consciousness much more accessible scientifically, and, in particular, the study of disorders, disruptions, and disturbances of consciousness has added tremendously to our understanding of the biological basis of human consciousness. The publication of a new edition is both critical and timely for continued understanding of the field of consciousness. In this critical and timely update, revised and new contributions by internationally renowned researchers—edited by the leaders in the field of consciousness research—provide a unique and comprehensive focus on human consciousness. The new edition of The Neurobiology of Consciousness will continue to be an indispensable resource for researchers and students working on the cognitive neuroscience of consciousness and related disorders, as well as for neuroscientists, psychologists, psychiatrists, and neurologists contemplating consciousness as one of the philosophical, ethical, sociological, political, and religious questions of our time. New chapters on the neuroanatomical basis of consciousness and short-term memory, and expanded coverage of comas and neuroethics, including the ethics of brain death The first comprehensive, authoritative collection to describe disorders of consciousness and how they are used to study and understand the neural correlates of conscious perception in humans. Includes both revised and new chapters from the top international researchers in the field, including Christof Koch, Marcus Raichle, Nicholas Schiff, Joseph Fins, and Michael Gazzaniga

Download or read book The Handbook of Brain Theory and Neural Networks written by Michael A. Arbib and published by MIT Press. This book was released on 2003 with total page 1328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).

Download or read book Statistical Inference in Modeling Longitudinal Data with Feedforward Neural Networks written by Joseph Brian Adams and published by . This book was released on 1997 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Inference of Properties of Distributions written by Jiantao Jiao and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern data science applications--ranging from graphical model learning to image registration to inference of gene regulatory networks--frequently involve pipelines of exploratory analysis requiring accurate inference of a property of the distribution governing the data rather than the distribution itself. Notable examples of properties include mutual information, Kullback--Leibler divergence, total variation distance, the entropy rate, among others. This thesis makes contributions to the performance, structure, and deployment of minimax rate-optimal estimators for a large variety of properties in high-dimensional and nonparametric settings. We present general methods for constructing information theoretically near-optimal estimators, and identify the corresponding limits in terms of the parameter dimension, the mixing rate (for processes with memory), and smoothness of the underlying density (in the nonparametric setting). We employ our schemes on the Google 1 Billion Word Dataset to estimate the fundamental limit of perplexity in language modeling, and to improve graphical model learning. The estimators are efficiently computable and exhibit a ``sample size enlargement'' phenomenon, i.e., they attain with $n$ samples what prior methods would have needed $n\log n$ samples to achieve. We provide a brief survey on our utilization and development of relevant tools from approximation theory, probability theory, and functional analysis.

Download or read book Computational and Network Modeling of Neuroimaging Data written by Kendrick Kay and published by Elsevier. This book was released on 2024-06-17 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuroimaging is witnessing a massive increase in the quality and quantity of data being acquired. It is widely recognized that effective interpretation and extraction of information from such data requires quantitative modeling. However, modeling comes in many diverse forms, with different research communities tackling different brain systems, different spatial and temporal scales, and different aspects of brain structure and function. Computational and Network Modeling of Neuroimaging Data provides an authoritative and comprehensive overview of the many diverse modeling approaches that have been fruitfully applied to neuroimaging data. This book gives an accessible foundation to the field of computational and network modeling of neuroimaging data and is suitable for graduate students, academic researchers, and industry practitioners who are interested in adopting or applying model-based approaches in neuroimaging. Provides an authoritative and comprehensive overview of major modeling approaches to neuroimaging data Written by experts, the book's chapters use a common structure to introduce, motivate, and describe a specific modeling approach used in neuroimaging Gives insights into the similarities and differences across different modeling approaches Analyses details of outstanding research challenges in the field

Download or read book Exploiting Biology s Structure written by Barbara Ann Wendelberger and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biology consistently demonstrates the correlation between anatomical structure and physiological function. Blood oxygenation levels measured in functional magnetic resonance imaging (fMRI) are used to infer regions of functional activation in the brain's gray matter, while measured water diffusion in diffusion tensor imaging (DTI) can be used to infer the structural location of myelinated white matter tracts. Effective connectivity modeling in neuroimaging, which estimates directed neural network models, has historically focused almost exclusively on the analysis of fMRI. The well-established association between anatomy and physiology suggests that incorporating structural information into functional data models could improve both the estimation and understanding of neurobiological networks. In neuroimaging, this idea can be tested by combining structural information from DTI with fMRI to investigate effective connectivity estimates using dynamic causal modeling (DCM). DCM incorporates statistical inference with biophysical modeling to estimate directed neural networks from fMRI data using an input-state-output model and a fully Bayesian approach. Default DCM analyses in Matlab utilize Gaussian shrinkage priors in the initialization of the neuronal state equations. A previous study suggests that increasing the variance of these shrinkage priors, based on the probability of an anatomical connection, improves the functional MRI effective connectivity estimates, as determined by Bayesian model selection (BMS). The statistical methods presented here explore the impact of the DCM prior means on neuronal connectivity estimates and investigate the degree to which DCMs might profit from the inclusion of detailed quantitative anatomical connectivity knowledge. Modeling that more accurately represents both the brain's anatomy and its physiology will improve the estimates of and inferences on brain connectivity networks. Further understanding of network connectivity in the brain paves the way for more effective treatments and therapies aimed at improving patients' quality of life, particularly in pathological situations.