Investigation of the dendritic geometry of brain stem motoneurons with different functions using multivariant...

We give an account of an effort to make quantitative morphological distinctions between motoneurons innervating functionally different muscles in the trigeminal and facial motor nuclei of the frog. Six groups of neurons were considered in the two nuclei on the basis of their peripheral targets. One group consisted of neurons (n = 7) innervating the levator bulbi muscle, which separates the orbital cavity from the oral cavity. In the second, third and fourth groups, motoneurons (n = 27) innervating jaw closer muscles (temporalis, masseter, pterygoideus) were studied. Neurons (n = 6) innervating the submaxillary muscle comprised the fifth group. This muscle forms the muscular floor of the mouth. It is active in deglutition and contributes to the opening of the mouth. The sixth group is formed by neurons of the facial nucleus (n = 7), which innervate the depressor mandibulae muscle. This is the main opener of the mouth. Neurons were selectively stained by cobalt labelling through the muscle nerves and the morphometric values of successfully labelled neurons were fed into a IBM AT 386 computer through a digitizing tablet for three-dimensional reconstruction. Four neurons labelled directly through the motor root of the trigeminal nerve but innervating unidentified muscles were added to the investigation. Two sets of quantitative measurements were taken from the neurons. In the first set (neurometric data), 17 quantitative variables were measured in the perikaryon and the dendritic arbor. In the second set, 15 variables concerned with the orientation and shape of the dendritic tree, the relation of the perikaryon to the dendritic tree and the spatial expansion of dendrites were measured in the three dimensions of Cartesian space (product-moment data). The data were subjected to multivariant statistical analysis. First, they were partitioned with cluster analysis. The average linkage between groups algorithm and the cosine of vectors of variables, or the Pearson correlation similarity coefficients were used. Neurometric data and product-moment data were analysed separately and in combination, and six to seven clusters were considered. In each case, the majority of neurons innervating jaw closer muscles were grouped into clusters different from neurons innervating jaw opener muscles. The best separation of functionally different neurons was achieved with the neurometric data set. The groups of neurons obtained from cluster analysis were subjected to non-parametric discriminant analysis with the eight nearest-neighbour classification criterion, and the results were checked with a cross-validation technique.(ABSTRACT TRUNCATED AT 400 WORDS)