A. Mizrahi et al., Comparative analysis of dendritic architecture of identified neurons usingthe Hausdorff distance metric, J COMP NEUR, 422(3), 2000, pp. 415-428
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.