Stereological methods provide researchers with unparalleled quantitative data from tissue samples and allow for well-evidenced research advances in a broad range of scientific fields. Presenting a concise introduction to the methodology and application of stereological research in neuroscience, Neurostereology provides a fuller understanding of the use of these methods in research and a means for replicating successful scientific approaches. Providing sound footing for future research, Neurostereology is a useful tool for basic and clinical researchers and advanced students looking to integrate these methods into their research.

Stereological techniques allow biologists to create quantitative, three-dimensional descriptions of biological structures from two- dimensional images of tissue viewed under the microscope. For example, they can accurately estimate the size of a particular organelle, the total length of a mass of capillaries, or the number of neurons or synapses in a particular region of the brain. This book provides a practical guide to designing and critically evaluating stereological studies of the nervous system and other tissues. It explains the basic concepts behind design-based stereology and how to get started. Also included are detailed descriptions of how to prepare tissue appropriately, perform pilot studies and decide on the appropriate sampling strategy, and account for phenomena such as tissue shrinkage. Numerous examples of applications of stereological methods that are applicable to studies of the central system and a wide variety of other tissues are explained. The book is therefore essential reading for neurobiologists and cell biologists interested in generating accurate representations of cell and tissue architecture.

Unbiased Stereology, Second Edition is a practical guide to making unbiased 3-D measurements via the microscope. Only those stereological techniques which have been tried and tested by real application are included. Although this technology is essentially mathematical and statistical, the authors do not immerse the reader in complex analysis, but rather provide simple heuristic explanations and references to the original proof, and illustrate the theory by analogies drawn from everyday experience. To give practical experience in application of the techniques, exercises are provided at the end of each chapter, complete with detailed worked answers.

"An important book for the libraries of professionals in neurobiology, neurology, and neurosurgery." -- Doody's Book Review Service

This update to Peter R. Mouton's pioneering work provides bioscientists with the concepts needed in order to apply the principles and practices of unbiased stereology to research involving biological tissues. Mouton starts with a brief explanation of the history and theory of the process before defining the terms, concepts, and tools of unbiased stereological procedures. He compares and contrasts the procedures with less-exacting approaches to quantitative analysis of biological structure using specific examples from biomedical literature. The book incorporates existing best practices with new methodologies, such as the Rare Event Protocol, while simplifying the dense, often difficult literature on the Subject to show the utility and importance of unbiased stereology. This clear, insightful guide goes a step further than other books on this Subject by demonstrating not only how to use unbiased stereology but also how to interpret and present the results. Written by the official U.S. representative to the International Society for Stereology, this is the most complete, up-to-date resource on the science of unbiased stereology. Those new to bioscience research as well as experienced practitioners will find that Mouton's explanations are the perfect companion for stereology courses and workshops.

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.

Recognition that aging is not the accumulation of disease, but rather comprises fundamental biological processes that are amenable to experimental study, is the basis for the recent growth of experimental biogerontology. As increasingly sophisticated studies provide greater understanding of what occurs in the aging brain and how these changes occur