Comparative analysis of dendritic architecture of identified neurons usingthe Hausdorff distance metric

Dendritic trees often are complex, three-dimensional structures. Comparativ e morphologic studies have not yet provided a reliable measure to analyze a nd compare the geometry of different dendritic trees. Therefore, it is impo rtant to develop quantitative methods for analyzing the three-dimensional g eometry of these complex trees. The authors developed a comparison measure based on the Hausdorff distance for comparing quantitatively the three-dime nsional structure of different neurons. This algorithm was implemented and incorporated into a new software package that the authors developed called NeuroComp. The authors tested this algorithm to study the variability in th e three-dimensional structure of identified central neurons as well as meas uring the structural differences between homologue neurons. They took advan tage of the uniform dendritic morphology of identified interneurons of an i nsect, the giant interneurons of the cockroach. More specifically, after es tablishing a morphometric data base of these neurons, the authors found tha t the algorithm is a reliable tool for distinguishing between dendritic tre es of different neurons, whereas conventional metric analysis often is inad equate. The authors propose to use this method as a quantitative tool for t he investigation of the effects of various experimental paradigms on three- dimensional dendritic architecture. J. Comp. Neurol. 422:415-428, 2000. (C) 2000 Wiley-Liss, Inc.