Connectivity between language related Brodmann areas derived from analyses of data source provided by Conel (1939-1967) has been proposed. The analysis consists of computing the correlation coefficients between each layer of one cortical area to each layer of another cortical area over the 8 age points. A "connection" was created between two layers of two cortical areas if: 1) its z-score have significance level less than 20%, and 2) the two layers began myelinating at the same age point. Predicted connections are consistent with neural network models derived neuro-imaging, psychological tests, and also support some seemingly unusual findings reported by others.

Neuron count in various brain structures is an important factor in many neurobiological studies. We describe a machine vision system which uses color images for the automated classification and counting of neurons in tissue samples. Samples are sliced into registered sections whose thickness is on the order of the diameter of a neuronal nucleus. Sections are stained so that the spectral transmission functions of the neuronal nuclei differ from the surrounding tissue. Each section is imaged using a light microscope. A Bayesian classifier is used for pixel labeling and a geometric analysis routine is employed to segment neuron regions in each section. The 3-D tissue sample is reconstructed using registered neuron regions from each section. An object-oriented database management system provides an efficient framework for cataloging neuron classes. Experimental results are presented and compared with results obtained by a histologist.

Neuron count in various brain structures is an important factor in many neurobiological studies. We describe a machine vision system which uses color images for the automated classification and counting of neurons in tissue samples. Samples are sliced into registered sections whose thickness is on the order of the diameter of a neuronal nucleus. Sections are stained so that the spectral transmission functions of the neuronal nuclei differ from the surrounding tissue. Each section is imaged using a light microscope. A Bayesian classifier is used for pixel labeling and a geometric analysis routine is employed to segment neuron regions in each section. The 3D tissue sample is reconstructed using registered neuron regions from each section. An object oriented database management system provides an experimental framework for cataloging neuron classes. Experimental results are presented and compared with results obtained by a histologist.