Data Science for Neuroimaging - by Ariel Rokem & Tal Yarkoni (Paperback)

About the Book

"Like many other research fields, over the last two decades neuroscience has turned towards data-driven discovery, a change which has dramatically reshaped the field. Through large collaborative projects and concerted data collection and data sharing efforts, the field is gaining access to large and heterogeneous data sets, at scales that have never been possible before. While these data present tremendous opportunities, their effective management, storage, and analysis presents serious challenges for many researchers. The tools and techniques of data science - a field which draws on software engineering, statistics, and machine learning to increase the efficiency and reproducibility of data extraction and analysis - have much to offer neuroscientists, but unfortunately these concepts are not taught within the standard neuroscience curriculum. This book offers an introduction to contemporary data science and its application in neuroimaging research. Taking a "hands-on" approach, the book explains common methods and approaches in a reader-friendly style, and includes numerous applications to openly available neuroscience datasets, including extensive code examples in Python. In contrast to most other neuroimaging-focused books, which place heavy emphasis on the process of acquiring and statistically analyzing neuroimaging data, the focus of this book is on developing and managing scalable and reproducible data analysis pipelines, broadly relevant skills that will readily translate to students' own research questions. Throughout, there is an emphasis on best-practices in data sharing and reporting, including how to apply principles of fairness, accountability, and transparency in neuroscience applications"--

Book Synopsis

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

Review Quotes

"I would absolutely recommend this book, not just for those wanting to do neuroimagining analyses, but for anyone who wants to do any serious scientific computing using Python. The well-selected exercises ensure that both undergraduate and graduate students will find engaging and thorough learning experiences throughout this book."---Jonathan Shock, Mathemafrica

About the Author

Ariel Rokem is research associate professor at the University of Washington Department of Psychology and Data Science Fellow at the University of Washington eScience Institute. He is a contributor to Python open-source tools for scientific computing and directs the NIH-funded Summer Institute for Neuroimaging and Data Science. Tal Yarkoni is a data scientist and research professor in the Department of Psychology at the University of Texas at Austin. His academic work focuses on developing new tools and methods for the analysis of psychology and neuroimaging data.