1	A Refined Method of Quantitative Cortical Analysis
2	Advantages of a quantitative cortical analysis technique: Characterization of neurological mutant mouse models with cortical phenotypes of varying severity (Lis1, Reelin, Dab1, Vldlr, Lrp8 (ApoER2)). Identification of subtle cortical phenotypes among various mouse genotypes. Insight into neurodevelopmental signaling pathways. Do different genetic combinations improve or worsen cortical phenotypes ?
3	Technique Overview Section brains (saggital) (P20 to adult). Stain sections with layer specific markers (Foxp2 – layer 6). Mount onto slides. Acquire and process images. Quantify neuronal positions and population distributions. Compare cumulative data (histograms, probability distribution curves). Test for significant differences among groups.
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5	Image Processing Import images into Adobe Photoshop. Crop, rotate and enhance. Orient with subcortical region at top and pial surface at bottom. Threshold and select neurons. Automatic thresholding is possible if staining is distinct (FoxP2). Other probes may display non-specific background staining, necessitating manual neuronal selection (calbindin).
6	Technique Refinement: visualizing the subcortical boundary Problem: difficult to visualize the lower cortical boundary. Inconsistent or inaccurate delineation of the subcortical baseline among sections will skew neuronal distributions and lead to flawed measurements and comparisons. Solution: myelin basic protein stain. Used to visualize myelinated fibers in the subcortical region. Allows for a more accurate and consistent delineation of the subcortical boundary relative to the layer specific marker (FoxP2).
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8	Quantification Black and white images are imported into Metamorph imaging analysis software. Cells are thresholded and counted by the program. Cortical distance (y-axis distance from subcortical boundary to pial boundary) is normalized to 100. Y-axis coordinate (centroid-y) values are obtained for each thresholded cell. Positional values range from 0 (subcortical boundary) to 100 (pial boundary). Data is logged and exported for analysis.
9	Data Analysis Generate cumulative data sets for each animal or group of animals. Import data into SPSS software and plot neuronal distributions. Generate histograms. Generate probability distributions. Data sets of neuronal distributions can now be compared statistically.
10	Statistical analysis Cumulative data for separate groups (genotypes) are compared. Probability distributions are overlayed to graphically display phenotypic variations. The Kolmogorov-Smirnov (KS) Z test is applied to determine whether two distributions are significantly different (p<0.05). The KS test measures the maximum absolute difference between the observed cumulative distribution functions for two samples. The KS-test is non-parametric and makes no assumption about the distribution of data.
11	Test Validation To validate that the technique can accurately measure subtle phenotypic differences, statistical similarity between genotypically identical groups must be demonstrated. Control experiment– statistical comparison of two groups from a single WT litter.
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