Neuroimaging Methods and Applications – serie

Advances in Resting-State Functional MRI: Methods, Interpretation, and Applications gives readers with basic neuroimaging experience an up-to-date and in-depth understanding of the methods, opportunities, and challenges in rs-fMRI. The book covers current knowledge gaps in rs-fMRI, including "what are biologically plausible brain networks," "how to tell what part is noise," "how to perform quality assurance on the data," "what are the spatial and temporal limits of our ability to resolve FC," and "how to best identify network features related to individual differences or disease state".

This book is an ideal reference for neuroscientists, computational neuroscientists, psychologists, biomedical engineers, physicists and medical physicists. Both new and more advanced researchers alike will be able to discover new information distilled from the past decade of research to become well-versed in rs-fMRI-related topics.

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. As neuroimaging is witnessing a massive increase in the quality and quantity of data being acquired, this book gives an accessible foundation to the field of computational neuroimaging, suitable for graduate students, academic researchers, and industry practitioners who are interested in adopting or applying model-based approaches in neuroimaging.It is widely recognized that effective interpretation and extraction of information from complex data requires quantitative modeling. However, modeling the brain 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. This book takes a critical step towards synthesizing and integrating across different modeling approaches.

The field of neuroimaging with functional magnetic resonance imaging (fMRI) is developing at a rapid pace, with a seemingly endless number of software packages, statistical methods, and different ways to organize and analyze neuroimaging data. Among such a wide variety of options, and with so many seemingly conflicting pieces of advice on the “correct” way of analyzing neuroimaging data, knowing what decisions to make is a difficult task.

Modern fMRI: Practical Lessons and Insights provides an up-to-date, holistic overview of the field of fMRI, familiarizing the reader with the latest trends in neuroimaging, such as standardized data organization and preprocessing, advances in functional connectivity and machine learning, and current guidelines in data and code sharing. This includes advice about best practices in preprocessing, statistical modeling, QA checks, and some of the latest tools and concepts to be familiar with, including fMRIPrep, OpenNeuro.org, Open Science practices, and Jupyter notebooks