Statistical and Computational Methods in Brain Image Analysis -

. .

Résumé :
The massive amount of nonstandard high-dimensional brain imaging data being generated 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(R) 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.

Biographie:
Moo K. Chung, Ph.D. is an associate professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison. He is also affiliated with the Waisman Laboratory for Brain Imaging and Behavior. He has won the Vilas Associate Award for his applied topological research (persistent homology) to medical imaging and the Editor's Award for best paper published in Journal of Speech, Language, and Hearing Research. Dr. Chung received a Ph.D. in statistics from McGill University. His main research area is computational neuroanatomy, concentrating on the methodological development required for quantifying and contrasting anatomical shape variations in both normal and clinical populations at the macroscopic level using various mathematical, statistical, and computational techniques.

Sommaire:
Introduction to Brain and Medical Images Image Volume Data Surface Mesh Data Landmark Data Vector Data Tensor and Curve Data Brain Image Analysis Tools Bernoulli Models for Binary Images Sum of Bernoulli Distributions Inference on Proportion of Activation MATLAB Implementation General Linear Models General Linear Models Voxel-Based Morphometry Case Study: VBM in Corpus Callosum Testing Interactions Gaussian Kernel Smoothing Kernel Smoothing Gaussian Kernel Smoothing Numerical Implementation Case Study: Smoothing of DWI Stroke Lesions Effective FWHM Checking Gaussianness Effect of Gaussianness on Kernel Smoothing Random Fields Theory Random Fields Simulating Gaussian Fields Statistical Inference on Fields Expected Euler Characteristics Anisotropic Kernel Smoothing Anisotropic Gaussian Kernel Smoothing Probabilistic Connectivity in DTI Riemannian Metric Tensors Chapman-Kolmogorov Equation Cholesky Factorization of DTI Experimental Results Discussions Multivariate General Linear Models Multivariate Normal Distributions Deformation-Based Morphometry (DBM) Hotelling's T2 Statistic Multivariate General Linear Models Case Study: Surface Deformation Analysis Cortical Surface Analysis Introduction Modeling Surface Deformation Surface Parameterization Surface-Based Morphological Measures Surface-Based Diffusion Smoothing Statistical Inference on the Cortical Surface Results Discussion Heat Kernel Smoothing on Surfaces Introduction Heat Kernel Smoothing Numerical Implementation Random Field Theory on Cortical Manifold Case Study: Cortical Thickness Analysis Discussion Cosine Series Representation of 3D Curves Introduction Parameterization of 3D Curves Numerical Implementation Modeling a Family of Curves Case Study: White Matter Fiber Tracts Discussion Weighted Spherical Harmonic Representation Introduction Spherical Coordinates Spherical Harmonics Weighted-SPHARM Package Surface Registration Encoding Surface Asymmetry Case Study: Cortical Asymmetry Analysis Discussion Multivariate Surface Shape Analysis Introduction Surface Parameterization Weighted Spherical Harmonic Representation Reduction of Gibbs Phenomenon Surface Normalization Image and Data Acquisition Results Discussion Numerical Implementation Laplace-Beltrami Eigenfunctions for Surface Data Introduction Heat Kernel Smoothing Generalized Eigenvalue Problem Numerical Implementation Experimental Results Case Study: Mandible Growth Modeling Conclusion Persistence Homology Introduction Rips Filtration Heat Kernel Smoothing of Functional Signal Min-max Diagram Case Study: Cortical Thickness Analysis Discussions Sparse Networks Introduction Massive Univariate Methods Why Sparse Models Are Needed? Persistent Structures for Sparse Correlations Persistent Structures for Sparse Likelihood Case Study: Application to Persistent Homology Sparse Partial Correlations Summary Sparse Shape Models Introduction Amygdala and Hippocampus Shape Models Data Set Sparse Shape Representation Case Study: Subcortical Structure Modeling Statistical Power Power under Multiple Comparisons Conclusion Modeling Structural Brain Networks Introduction DTI Acquisition and Preprocessing I -neighbor Construction Node Degrees Connected Components I -filtration Numerical Implementation Discussions Mixed Effect Models Introduction Mixed Effects Models Bibliography