Bland, J.M. and Altman, D. G. (1986). Statistical methods for assessing the concordance between two clinical measurement methods. Lancet 1, 307-310 doi: 10.1016/S0140-6736 (86)90837-8 The objective of this project was to determine (1), whether manual and automated methods of identifying single and doubly colored nuclei in suspension volumes of homogenized tissue samples resulted in statistically incomparable estimates of the total number of cells and neurons from easily identifiable regions of interest in flattened cortical preparations (2) compare these results to estimates of the same parameters in the same region of interest of the other hemisphere in an isolated individual using stereological methods on cut tissues and (3) determine the distribution of particles in the z-domain of nissl-colored sections. Based on the first objective, we determined manual counts and automated flow counts from at least two aliquots of each homogenized sample of the flattened cortex used in this study (n = 61, Table 1). Next, we determined the concordance within the measurements by calculating the nonparametric T statistics during repeated measurements of each aliquot and testing the difference between the mean values, so that a P-value of less than 0.05 was taken to indicate a significant difference between the measurements (Table 2). We then used the Bland and Altman (1986) approach to compare the differences between measurements and measurement means in our analysis of the isotropic fractionator and current fractionator data (Figures S1-S4). In addition, we cover the correlation of Lin Concordance, a statistic that approximates variation in linear regression data (Lin, 1989) (Table 2). We calculated the density of cells or neurons per cubic millimeter by taking the estimate of the parameters of the sample divided by the measured volume of this sample, according to the second objective, which is to compare the results of flattened cortex samples with the results of coronal brain incisions. For example, when the optical splitter is used, the examiner draws up an overview of the area of interest in each of the 10 sections selected for analysis.

The Cavalieri method then uses this outline to estimate the surface area of this section. The volume of the section is then calculated by multiplying the surface area by the thickness of the section, as it was initially cut on the freezing microtone. The density of cells or neurons is calculated by deflecting the number of cells or neurons estimated with the optical fractionator by the volume of each section determined according to the Cavalieri method. . . .