title: Design-based stereology in neuroscience.
abstract: Quantitative morphology of the CNS has recently undergone major developments. In particular, several new approaches, known as design-based stereologic methods, have become available and have been successfully applied to neuromorphological research. However, much confusion and uncertainty remains about the meaning, implications, and advantages of these design-based stereologic methods. The objective of this review is to provide some clarification. It does not comprise a full description of all stereologic methods available. Rather, it is written by users for users, provides the reader with a guided tour through the relevant literature. It has been the experience of the authors that most neuroscientists potentially interested in design-based stereology need to analyze volumes of brain regions, numbers of cells (neurons, glial cells) within these brain regions, mean volumes (nuclear, perikaryal) of these cells, length densities of linear biological structures such as vessels and nerve fibers within brain regions, and the cytoarchitecture of brain regions (i.e. the spatial distribution of cells within a region of interest). Therefore, a comprehensive introduction to design-based stereologic methods for estimating these parameters is provided. It is demonstrated that results obtained with design-based stereology are representative for the entire brain region of interest, and are independent of the size, shape, spatial orientation, and spatial distribution of the cells to be investigated. Also, it is shown that bias (i.e. systematic error) in results obtained with design-based stereology can be limited to a minimum, and that it is possible to assess the variability of these results. These characteristics establish the advantages of design-based stereologic methods in quantitative neuromorphology.
authors.affiliation: Department of Psychiatry and Neuropsychology, Division of Cellular Neuroscience, University of Maastricht, P.O. Box 616, NL-6200 MD Maastricht, Netherlands. c.schmitz@np.unimaas.nl
authors.forename: C
authors.initials: C
authors.lastname: Schmitz
authors.forename: P R
authors.initials: PR
authors.lastname: Hof
mesh_terms: D000818:Animals; D001921:Brain / Q000166:cytology* / Q000502:physiology; D002452:Cell Count / Q000295:instrumentation / Q000379:methods / Q000639:trends; D048430:Cell Shape / Q000502:physiology; D006801:Humans; D019044:Image Cytometry / Q000295:instrumentation / Q000379:methods* / Q000639:trends; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods* / Q000639:trends; D008853:Microscopy / Q000295:instrumentation / Q000379:methods* / Q000639:trends; D009445:Neuroanatomy / Q000295:instrumentation / Q000379:methods* / Q000639:trends; D009474:Neurons / Q000166:cytology / Q000502:physiology; D015983:Selection Bias; D012984:Software / Q000639:trends