journal article·2011-05-03Open access

STEREOLOGY OF NEURONAL CONNECTIONS (MYELINATED FIBERS OF WHITE MATTER AND SYNAPSES OF NEOCORTEX) IN HUMAN BRAIN

Yong Tang, Jens R Nyengaard, Bente Pakkenberg, Hans Jørgen G Gundersen

Image Analysis & Stereology· Volume 22· Issue 1

Abstract

Unbiased stereological sampling and counting techniques for estimating the total length, total volume and diameter distribution of myelinated nerve fibers in white matter and the total number of synapses in neocortex of human autopsy brains were described. Uniform random sampling of tissues from entire hemisphere was performed. The total volume and total length of myelinated fibers in white matter were estimated from the product of the volume of white matter obtained with the Cavalieri principle and the volume density and length density of myelinated fibers in white matter, respectively. The volume density of myelinated nerve fibers in white a matter was estimated with a point counting method. The length density of myelinated fibers in white matter was estimated from the isotropic, uniform random sections that were ensured by the sector. The diameter of myelinated fibers was derived by measuring the profile diameterperpendicular to its longest axis. The ethanolic phosphotungstic acid staining technique was modified for staining synapses in human autopsy brains. The total number of synapses in each neocortical region was estimated as the product of the volume of each neocortical region and the numerical density of synapses in each region. The numerical density of synapses in each neocortical region was obtained with the disector at the electron microscopical level. The presented methods will be useful for quantitative studies of the changes of myelinated nerve fibers and synapses in various distinct regions of the central nervous system due to development, aging and diseases.
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Research context

OpenAlex topics: Point processes and geometric inequalities · Alzheimer's disease research and treatments · Morphological variations and asymmetry

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