This book is the story of the marriage of a new techl}ology, computers, with an old problem, the study of neuroanatomical structures using the light microscope. It is aimed toward you, the neuroanatomist, who until now have used computers primarily for word processing but now wish to use them also to collect and analyze your laboratory data. Mter reading the book, you will be better equipped to use a computer system for data collection and analysis, to employ a programmer who might develop a system for you, or to evaluate the systems available in the marketplace. To start toward this goal, a glossary first presents commonly used terms in computer assisted neuroanatomy. This, on its own, will aid you as it merges the jargon of the two different fields. Then, Chapter 1 presents a historical review to describe the manual tasks involved in presenting and measuring anatomic structures. This review lays a base line of the tasks that were done before computers and the amount of skill and time needed to perform the tasks. In Chapters 2 and 3, you will find basic information about laboratory computers and programs to the depth required for you to use the machines easily and talk with some fluency to computer engineers, programmers, and salesmen. Chapters 4, 5, and 6 present the use of computers to reconstruct anatomic structures, i.e., to enter them into a computer memory, where they are later displayed and analyzed.

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.

In Computational Neuroanatomy: Principles and Methods, the path-breaking investigators who founded the field review the principles and key techniques available to begin the creation of anatomically accurate and complete models of the brain. Combining the vast, data-rich field of anatomy with the computational power of novel hardware, software, and computer graphics, these pioneering investigators lead the reader from the subcellular details of dendritic branching and firing to system-level assemblies and models.

Neuroanatomical Research Techniques discusses developments in major neuroanatomical research techniques. The book is organized into four parts. Part I deals generally with the preparation and study of brain tissue. It includes a chapter on the microscope, discussing optical magnification, limitations of microscopy, and optical contrasting methods. Other chapters summarize basic techniques for tissue preparation and sectioning; present guidelines for a number of standard, but essential, staining procedures; and present sophisticated and contemporary computer techniques that are proving to be invaluable as neuroanatomy evolves from a qualitative to a quantitative discipline. Part II deals with techniques often used for the study of normal tissue. These include the Golgi method, fluorescence histochemistry, techniques for staining single neurons, and the use of the electron microscope. Part III presents techniques for studying intrinsic connections of the nervous system. These include techniques for silver impregnation of degenerating fibers; autoradiographic technique for studying axonal projections; and somatopetal movement of horseradish peroxidase as a tool for studying connections and neuron morphology. Part IV discusses the interpretation of results from neuroanatomical research techniques and presents examples of the applications of neuroanatomical methods to major problems in physiological psychology.

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® 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.

One of the major challenges of modern neuroscience is to define the complex pattern of neural connections that underlie cognition and behaviour. This atlas capitalises on novel diffusion MRI tractography methods to provide a comprehensive overview of connections derived from virtual in vivo tractography dissections of the human brain.

The first two editions of this title had a tremendous impact in neuroscience. Between the Second edition in 1989 and today, there has been an explosion of information in the field, including advances in molecular techniques, such as genomics and proteomics, which have become increasing important in neuroscience. A renaissance in fluorescence has occurred, driven by the development of new probes, new microscopes, live imagers, and computer processing. The introduction of new markers has enormously stimulated the field, moving it from tissue culture to neurophysiology to functional MRI techniques.