A contribution to the electron microscopic morphometric analysis of peripheral nerve.

Several aspects of data collection and analyses of peripheral nerve experiments employing light and electron microscopic morphometric techniques have not been adequately discussed in the literature. From statistical tests performed on nerve data, it was found that light compared with electron microscopic morphometry underestimates the number of small fibers. An optimum sampling strategy must take into account a potential bias toward small fibers introduced by measuring fibers from electronmicrographs. It must also take into account a potential bias introduced by the non-random distribution of nerve fibers of different sizes in nerves. These biases are offset by sampling a large enough number of fibers from large enough area electron micrographs. A method is presented for analysing periopheral nerve data using the nested analysis of variance. This requires first dividing the usual bimodal nerve fiber distribution into component normally distributed parts. The number of fibers in the two portions of a bimodal distribution must be considered in data analysis. Knowledge of the variances of parameters to be studied in any particular nerve is necessary for optimum sampling strategies.