Data Science For Neuroimaging
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Data Science For Neuroimaging
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Author : Ariel Rokem
language : en
Publisher: Princeton University Press
Release Date : 2023-11-07
Data Science For Neuroimaging written by Ariel Rokem and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-07 with Science categories.
Data science methods and tools—including programming, data management, visualization, and machine learning—and their application to neuroimaging research As neuroimaging turns toward data-intensive discovery, researchers in the field must learn to access, manage, and analyze datasets at unprecedented scales. Concerns about reproducibility and increased rigor in reporting of scientific results also demand higher standards of computational practice. This book offers neuroimaging researchers an introduction to data science, presenting methods, tools, and approaches that facilitate automated, reproducible, and scalable analysis and understanding of data. Through guided, hands-on explorations of openly available neuroimaging datasets, the book explains such elements of data science as programming, data management, visualization, and machine learning, and describes their application to neuroimaging. Readers will come away with broadly relevant data science skills that they can easily translate to their own questions. • Fills the need for an authoritative resource on data science for neuroimaging researchers • Strong emphasis on programming • Provides extensive code examples written in the Python programming language • Draws on openly available neuroimaging datasets for examples • Written entirely in the Jupyter notebook format, so the code examples can be executed, modified, and re-executed as part of the learning process
The Statistical Analysis Of Functional Mri Data
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Author : Nicole Lazar
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-06-10
The Statistical Analysis Of Functional Mri Data written by Nicole Lazar and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-10 with Medical categories.
The study of brain function is one of the most fascinating pursuits of m- ern science. Functional neuroimaging is an important component of much of the current research in cognitive, clinical, and social psychology. The exci- ment of studying the brain is recognized in both the popular press and the scienti?c community. In the pages of mainstream publications, including The New York Times and Wired, readers can learn about cutting-edge research into topics such as understanding how customers react to products and - vertisements (“If your brain has a ‘buy button,’ what pushes it?”, The New York Times,October19,2004),howviewersrespondtocampaignads(“Using M. R. I. ’s to see politics on the brain,” The New York Times, April 20, 2004; “This is your brain on Hillary: Political neuroscience hits new low,” Wired, November 12,2007),howmen and womenreactto sexualstimulation (“Brain scans arouse researchers,”Wired, April 19, 2004), distinguishing lies from the truth (“Duped,” The New Yorker, July 2, 2007; “Woman convicted of child abuse hopes fMRI can prove her innocence,” Wired, November 5, 2007), and even what separates “cool” people from “nerds” (“If you secretly like Michael Bolton, we’ll know,” Wired, October 2004). Reports on pathologies such as autism, in which neuroimaging plays a large role, are also common (for - stance, a Time magazine cover story from May 6, 2002, entitled “Inside the world of autism”).
Handbook Of Neuroimaging Data Analysis
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Author : Hernando Ombao
language : en
Publisher: CRC Press
Release Date : 2016-11-18
Handbook Of Neuroimaging Data Analysis written by Hernando Ombao and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-18 with Mathematics categories.
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.
Multivariate Analysis For Neuroimaging Data
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Author : Atsushi Kawaguchi
language : en
Publisher: CRC Press
Release Date : 2021-07-01
Multivariate Analysis For Neuroimaging Data written by Atsushi Kawaguchi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-01 with Mathematics categories.
This book describes methods for statistical brain imaging data analysis from both the perspective of methodology and from the standpoint of application for software implementation in neuroscience research. These include those both commonly used (traditional established) and state of the art methods. The former is easier to do due to the availability of appropriate software. To understand the methods it is necessary to have some mathematical knowledge which is explained in the book with the help of figures and descriptions of the theory behind the software. In addition, the book includes numerical examples to guide readers on the working of existing popular software. The use of mathematics is reduced and simplified for non-experts using established methods, which also helps in avoiding mistakes in application and interpretation. Finally, the book enables the reader to understand and conceptualize the overall flow of brain imaging data analysis, particularly for statisticians and data-scientists unfamiliar with this area. The state of the art method described in the book has a multivariate approach developed by the authors’ team. Since brain imaging data, generally, has a highly correlated and complex structure with large amounts of data, categorized into big data, the multivariate approach can be used as dimension reduction by following the application of statistical methods. The R package for most of the methods described is provided in the book. Understanding the background theory is helpful in implementing the software for original and creative applications and for an unbiased interpretation of the output. The book also explains new methods in a conceptual manner. These methodologies and packages are commonly applied in life science data analysis. Advanced methods to obtain novel insights are introduced, thereby encouraging the development of new methods and applications for research into medicine as a neuroscience.
Studies In Neural Data Science
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Author : Antonio Canale
language : en
Publisher: Springer
Release Date : 2018-12-28
Studies In Neural Data Science written by Antonio Canale and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-28 with Mathematics categories.
This volume presents a collection of peer-reviewed contributions arising from StartUp Research: a stimulating research experience in which twenty-eight early-career researchers collaborated with seven senior international professors in order to develop novel statistical methods for complex brain imaging data. During this meeting, which was held on June 25–27, 2017 in Siena (Italy), the research groups focused on recent multimodality imaging datasets measuring brain function and structure, and proposed a wide variety of methods for network analysis, spatial inference, graphical modeling, multiple testing, dynamic inference, data fusion, tensor factorization, object-oriented analysis and others. The results of their studies are gathered here, along with a final contribution by Michele Guindani and Marina Vannucci that opens new research directions in this field. The book offers a valuable resource for all researchers in Data Science and Neuroscience who are interested in the promising intersections of these two fundamental disciplines.
Connectomics
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Author : Brent C. Munsell
language : en
Publisher: Academic Press
Release Date : 2018-09-08
Connectomics written by Brent C. Munsell and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-08 with Computers categories.
Connectomics: Applications to Neuroimaging is unique in presenting the frontier of neuro-applications using brain connectomics techniques. The book describes state-of-the-art research that applies brain connectivity analysis techniques to a broad range of neurological and psychiatric disorders (Alzheimer’s, epilepsy, stroke, autism, Parkinson’s, drug or alcohol addiction, depression, bipolar, and schizophrenia), brain fingerprint applications, speech-language assessments, and cognitive assessment. With this book the reader will learn: Basic mathematical principles underlying connectomics How connectomics is applied to a wide range of neuro-applications What is the future direction of connectomics techniques. This book is an ideal reference for researchers and graduate students in computer science, data science, computational neuroscience, computational physics, or mathematics who need to understand how computational models derived from brain connectivity data are being used in clinical applications, as well as neuroscientists and medical researchers wanting an overview of the technical methods. Features: Combines connectomics methods with relevant and interesting neuro-applications Covers most of the hot topics in neuroscience and clinical areas Appeals to researchers in a wide range of disciplines: computer science, engineering, data science, mathematics, computational physics, computational neuroscience, as well as neuroscience, and medical researchers interested in the technical methods of connectomics Combines connectomics methods with relevant and interesting neuro-applications Presents information that will appeal to researchers in a wide range of disciplines, including computer science, engineering, data science, mathematics, computational physics, computational neuroscience, and more Includes a mathematics primer that formulates connectomics from an applied point-of-view, thus avoiding difficult to understand theoretical perspective Lists publicly available neuro-imaging datasets that can be used to construct structural and functional connectomes
Machine Learning And Interpretation In Neuroimaging
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Author : Georg Langs
language : en
Publisher: Springer
Release Date : 2012-11-11
Machine Learning And Interpretation In Neuroimaging written by Georg Langs and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-11 with Computers categories.
Brain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. This state-of-the-art survey offers a collection of papers from the Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning.
Multivariate Analysis For Neuroimaging Data
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Author : Atsushi Kawaguchi
language : en
Publisher: CRC Press
Release Date : 2023-07
Multivariate Analysis For Neuroimaging Data written by Atsushi Kawaguchi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07 with Brain categories.
This book enables us to analyze statistically brain imaging data. It is meant for a wide range of researchers interested in biostatistics, data science, and neuroscience. It is useful to understand the background theory of standard software for neuroimaging data analysis.
Recent Advances And Challenges On Big Data Analysis In Neuroimaging
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Author : Jian Kang
language : en
Publisher: Frontiers Media SA
Release Date : 2017-05-17
Recent Advances And Challenges On Big Data Analysis In Neuroimaging written by Jian Kang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-17 with Electronic book categories.
Big data is revolutionizing our ability to measure and study the human brain. New technology increases the resolution of images that are being study as well as enables researchers to study the brain as it functions. These technological advances are combined with efforts to collect neuroimaging data on large numbers of subjects, in some cases longitudinally. This combination of advances in measurement and scope of studies requires novel development in the statistical analysis. Fast, scalable, robust and accurate models and approaches need to be developed to make headway on these problems. This volume represents a unique collection of researchers providing deep insights on the statistical analysis of big neuroimaging data.
Magnetic Resonance Brain Imaging
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Author : Jörg Polzehl
language : en
Publisher: Springer Nature
Release Date : 2019-09-25
Magnetic Resonance Brain Imaging written by Jörg Polzehl and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-25 with Medical categories.
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.