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.

This short book will discuss the crucial role that statistical modelling plays in unravelling patterns arising from recording neuronal cell activities (spikes). The history of modern neuroscience coincides with the rise and popularity of statistical thought process – dating back to the beginning of the 20th century. As such, and from early on, spike trains (incidence of spiking activities, mapped out on a time scale), have been viewed as realizations of point processes. Consequently, the advancement of new means of recording spike trains, such as simultaneous recordings of multiple cells, has radically increased the demand for appropriate statistical analysis, whether through developing theoretical bases for multivariate stochastics processes, or via expanding the tools for capturing the networking of adjacent neurons.

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.

This short book will discuss the crucial role that statistical modelling plays in unravelling patterns arising from recording neuronal cell activities (spikes). The history of modern neuroscience coincides with the rise and popularity of statistical thought process – dating back to the beginning of the 20th century. As such, and from early on, spike trains (incidence of spiking activities, mapped out on a time scale), have been viewed as realizations of point processes. Consequently, the advancement of new means of recording spike trains, such as simultaneous recordings of multiple cells, has radically increased the demand for appropriate statistical analysis, whether through developing theoretical bases for multivariate stochastics processes, or via expanding the tools for capturing the networking of adjacent neurons.