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.
Positron Emission Tomography: Some Analysis Methods
Structural Magnetic Resonance Imaging
Diffusion Magnetic Resonance Imaging
A Tutorial for Multisequence Clinical Structural Brain MRI
Principles of Functional Magnetic Resonance Imaging
Electroencephalography (EEG): Neurophysics, Experimental Methods, and Signal Processing
Statistical Methods and Models
Image Reconstruction in Functional MRI
Statistical Analysis on Brain Surfaces
Linear and Nonlinear Models for fMRI Time Series Analysis
Functional Neuroimaging Group Studies
Corrections for Multiplicity in Functional Neuroimaging Data
Functional Connectivity Analysis for fMRI Data
Multivariate Decompositions in Brain Imaging
Effective Connectivity and Causal Inference in Neuroimaging
Modeling Change in the Brain: Methods for Cross-Sectional and Longitudinal Data
Joint fMRI and DTI Models for Brain Connectivity
Statistical Analysis of Electroencephalograms
Advanced Topics for Modeling Electroencephalograms
"Handbook of Neuroimaging Data Analysis is a great source to help you get started . . . If you find a particular modality that interests you, just email one of the authors in the book who also works on data analysis within that modality. They are all friendly and helpful, and they will point you to sources of publically available data."
~Timothy D. Johnson
"These chapters are primarily written by statisticians, but the book is nicely balanced by contributions from biomedical engineers, psychologists, and cognitive scientists. . . I recommend this book to statisticians interested in learning about neuroimaging and contributing to its growth."
~Journal of the American Statistical Association