Biomedical subjects

A E Switala

Publications and source records attributed to A E Switala.

4 recordsLinked to original sources

PubMed2006

Minicolumnar structure in Heschl's gyrus and planum temporale: Asymmetries in relation to sex and callosal fiber number.

AIM: To investigate the cytoarchitectural basis of asymmetries in human auditory cortex. Minicolumn spacing and number, and regional cortical volume and surface area were measured in the primary auditory region (Heschl's gyrus, HG) and posterior auditory association region (planum temporale, PT) in 17 neurologically normal adults (10 female, seven male). PT surface area, minicolumn spacing and minicolumn number were greater in the left hemisphere. HG surface area was larger in the left hemisphere. Asymmetries of minicolumn number in primary and association auditory regions correlated with axonal fiber numbers in the subregions of the corpus callosum through which they project. PT minicolumn number was more asymmetrical in men than women but total number was similar in the two sexes. We conclude that asymmetry of the surface area of the PT is a function of minicolumn spacing. Fewer callosal projections between the plana are found when the minicolumn spacing is more asymmetrical.

Aged↗

PubMed2001

Morphological differences between minicolumns in human and nonhuman primate cortex.

Our study performed a quantitative investigation of minicolumns in the planum temporale (PT) of human, chimpanzee, and rhesus monkey brains. This analysis distinguished minicolumns in the human cortex from those of the other nonhuman primates. Human cell columns are larger, contain more neuropil space, and pack more cells into the core area of the column than those of the other primates tested. Because the minicolumn is a basic anatomical and functional unit of the cortex, this strong evidence showed reorganization in this area of the human brain. The relationship between the minicolumn and cortical volume is also discussed.

Adaptation, Physiological↗

PubMed2001

Lateralization of minicolumns in human planum temporale is absent in nonhuman primate cortex.

Gross analyses of large brain areas, as in MRI studies of macroanatomical structures, average subtle alterations in small regions, inadvertently missing significant anomalies. We developed a computerized imaging program to microscopically examine minicolumns and used it to study Nissl-stained slides of normal human, chimpanzee, and rhesus monkey brains in a region of the planum temporale. With this method, we measured the width of cell columns, the peripheral neuropil space, the spacing density of neurons within columns, and the Gray Level index per minicolumn. Only human brain tissue revealed robust asymmetry in two aspects of minicolumn morphology: wider columns and more neuropil space on the left side. This asymmetry was absent in chimpanzee and rhesus monkey brains.

Algorithms↗

PubMed2000

Quantitative analysis of cell columns in the cerebral cortex.

We present a quantified imaging method that describes the cell column in mammalian cortex. The minicolumn is an ideal template with which to examine cortical organization because it is a basic unit of function, complete in itself, which interacts with adjacent and distance columns to form more complex levels of organization. The subtle details of columnar anatomy should reflect physiological changes that have occurred in evolution as well as those that might be caused by pathologies in the brain. In this semiautomatic method, images of Nissl-stained tissue are digitized or scanned into a computer imaging system. The software detects the presence of cell columns and describes details of their morphology and of the surrounding space. Columns are detected automatically on the basis of cell-poor and cell-rich areas using a Gaussian distribution. A line is fit to the cell centers by least squares analysis. The line becomes the center of the column from which the precise location of every cell can be measured. On this basis several algorithms describe the distribution of cells from the center line and in relation to the available surrounding space. Other algorithms use cluster analyses to determine the spatial orientation of every column.

Algorithms↗