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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.
Geometry of Cortical Manifolds
Smoothing on Cortical Manifolds
Weighted Fourier Representation
Structural Brain Connectivity
Topological Data Analysis
Readership: Researchers and graduate students in the fields of computational neuroscience and brain imaging, medical image analysis and pattern recognition.
Includes necessary and very practical mathematical and statistical methodologies
Accessible to a wide range of readers and provides methodological understanding in a manner immediately usable to researchers
Covers various topics in great detail without sacrificing readability; it can be used as both a textbook for graduate students and a reference for researchers