Brain Network Analysis

Author: Moo K. Chung
Published in 2019 through Cambridge University Press
330 pages.

This book serves as a coherent overview of various statistical and mathematical approaches used in brain network analysis, where modeling the complex structures and functions of the human brain often poses many unique computational and statistical challenges. This book fills a gap as a textbook for graduate students while simultaneously articulating important and technically challenging topics. Whereas most available books are graph theory centric, this text introduces techniques arising from graph theory and expands to include other different models in its discussion on network science, regression, and algebraic topology. Links are included to the sample data and codes used in generating the book's results and figures, helping to empower methodological understanding in a manner immediately usable to both researchers and students.

Statistical and Computational Methods in Brain Image Analysis. 

Author: Moo K. Chung
Published in  2013 through CRC Press
416 pages

The massive amount of nonstandard high-dimensional brain imaging data is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data. The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author’s website. By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics.

Computational Neuroanatomy: The Methods

Author: Moo K. Chung
Published in 2012 through World Scientific Publishing
420 pages.

Computational neuroanatomy is an emerging field that utilizes various non-invasive brain imaging modalities, such as MRI and DTI, in quantifying the spatiotemporal dynamics of the human brain structures in both normal and clinical populations. This discipline emerged about twenty years ago and has made substantial progress in the past decade. The main goals of this book are to provide an overview of various mathematical, statistical and computational methodologies used in the field to a wide range of researchers and students, and to address important yet technically challenging topics in further detail.

Table of contents

Statistical preliminary
Brain network nodes and edges
Graph theory
Correlation networks
Big brain networks
Network simulations
Persistent homology
Diffusion on graphs
Sparse networks
Brain network distances
Combinatorial inferences for networks
Series expansion of connectivity matrices
Dynamic network models

Book Review

"The writing style is pleasing and the book has the important virtue of using a consistent mathematical notation and terminology throughout the book, unlike collections of chapters from various authors that are usually published on this kind of topic. ... This provides an excellent supplement and will appeal to students starting in the field as well as researchers wanting to refresh their knowledge or learn more about some aspects of brain analysis. … a very good book to have in a lab, and it is a pleasure to recommend it." ―Australian & New Zealand Journal of Statistics, 56(4), 2014

"… a great new reference text to the field of structural brain imaging. The presence of MATLAB code will make it easy for people to play around with the various data formats and more easily get involved in this exciting field. As a researcher already involved in neuroimaging data analysis, I have a feeling that this is a book I will return to often as a reference source, and I am happy to have it as part of my library." ―Martin A. Lindquist, Journal of the American Statistical Association, September 2014, Vol. 109

Table of contents

Statistical Preliminary
Deformation-Based Morphometry
Tensor-Based Morphometry
Voxel-Based Morphometry
Geometry of Cortical Manifolds
Smoothing on Cortical Manifolds
Surface-Based Morphometry
Weighted Fourier Representation
Structural Brain Connectivity
Topological Data Analysis