1.1 --- a/grant.html Wed Apr 22 06:43:51 2009 -0700 1.2 +++ b/grant.html Wed Apr 22 06:45:17 2009 -0700 1.3 @@ -433,19 +433,15 @@ 1.4 of a better map. The development of present-day cortical maps was driven by the application of histological 1.5 stains. If a different set of stains had been available which identified a different set of features, then today’s 1.6 cortical maps may have come out differently. It is likely that there are many repeated, salient spatial patterns 1.7 +in the gene expression which have not yet been captured by any stain. Therefore, cortical anatomy needs to 1.8 +incorporate what we can learn from looking at the patterns of gene expression. 1.9 _________________________________________ 1.10 7The sagittal data do not cover the entire cortex, and also have greater registration error[15]. Genes were selected by the Allen 1.11 Institute for coronal sectioning based on, “classes of known neuroscientific interest... or through post hoc identification of a marked 1.12 non-ubiquitous expression pattern”[15]. 1.13 8In both cases, the cause is that pairwise correlations between the gene expression of voxels in different areas but the same layer 1.14 are often stronger than pairwise correlations between the gene expression of voxels in different layers but the same area. Therefore, a 1.15 -pairwise voxel correlation clustering algorithm will tend to create clusters representing cortical layers, not areas (there may be clusters 1.16 -which presumably correspond to the intersection of a layer and an area, but since one area will have many layer-area intersection 1.17 -clusters, further work is needed to make sense of these). The reason that Gene Finder cannot the find marker genes for cortical areas 1.18 -is that, although the user chooses a seed voxel, Gene Finder chooses the ROI for which genes will be found, and it creates that ROI by 1.19 -(pairwise voxel correlation) clustering around the seed. 1.20 -in the gene expression which have not yet been captured by any stain. Therefore, cortical anatomy needs to 1.21 -incorporate what we can learn from looking at the patterns of gene expression. 1.22 +pairwise voxel correlation clustering algorithm will tend to create clusters representing cortical layers, not areas. 1.23 While we do not here propose to analyze human gene expression data, it is conceivable that the methods 1.24 we propose to develop could be used to suggest modifications to the human cortical map as well. In fact, the 1.25 methods we will develop will be applicable to other datasets beyond the brain. 1.26 @@ -486,10 +482,10 @@ 1.27 as a boolean mask over the surface pixels. 1.28 We calculated the correlation between each gene and each cortical area. The top row of Figure 2 shows the 1.29 three genes most correlated with area SS. 1.30 -__ 1.31 +Conditional entropy 1.32 +__________________ 1.33 9SEV is a sparse format for spatial data. It is the format in which the ABA data is made available. 1.34 -Conditional entropy 1.35 -Foreach region, we created and ran a forward stepwise procedure which attempted to find pairs of gene 1.36 +For each region, we created and ran a forward stepwise procedure which attempted to find pairs of gene 1.37 expression boolean masks such that the conditional entropy of the target area’s boolean mask, conditioned 1.38 upon the pair of gene expression boolean masks, is minimized. 1.39 This finds pairs of genes which are most informative (at least at these discretization thresholds) relative to the