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2.1 --- a/grant.html Mon Apr 13 23:11:04 2009 -0700 2.2 +++ b/grant.html Mon Apr 13 23:17:40 2009 -0700 2.3 @@ -256,21 +256,21 @@ 2.4 We downloaded the ABA data and applied a mask to select only those voxels which belong to cerebral cortex. We divided 2.5 the cortex into hemispheres. 2.6 Using Caret[1], we created a mesh representation of the surface of the selected region. For each gene, for each node of 2.7 -the mesh, we calculated an average of the gene expression of the voxels “underneath” that mesh node. Using Caret, we then 2.8 -flattened the cortex, creating a two-dimensional mesh. 2.9 +the mesh, we used Caret to calculate an average of the gene expression of the voxels “underneath” that mesh node. We 2.10 +then used Caret to flatten the cortex, creating a two-dimensional mesh. 2.11 We sampled the nodes of the irregular, flat mesh in order to create a regular grid of pixel values. We converted this grid 2.12 into a MATLAB matrix. 2.13 We manually traced the boundaries of each cortical area from the ABA coronal reference atlas slides. We then converted 2.14 these manual traces into Caret-format regional boundary data on the mesh surface. Using Caret, we projected the regions 2.15 onto the 2-d mesh, and then onto the grid, and then we converted the region data into MATLAB format. 2.16 -At this point, the data is in the form of a number of 2-D matrices, each registered to each other, with the matrix entries 2.17 -representing a grid of points (pixels) over the cortical surface: 2.18 +At this point, the data is in the form of a number of 2-D matrices, all in registration, with the matrix entries representing 2.19 +a grid of points (pixels) over the cortical surface: 2.20 ∙A 2-D matrix whose entries represent the regional label associated with each surface pixel 2.21 ∙For each gene, a 2-D matrix whose entries represent the average expression level underneath each surface pixel 2.22 -Rather than a single average expression level for each surface pixel, we plan to create a separate matrix for each cortical 2.23 -layer to represent the average expression level within that layer. Cortical layers are found at different depths in different 2.24 -parts of the cortex. In preparation for extracting the layer-specific datasets, we have extended Caret with routines that 2.25 -allow the depth of the ROI for volume-to-surface projection to vary. 2.26 +To move beyond a single average expression level for each surface pixel, we plan to create a separate matrix for each 2.27 +cortical layer to represent the average expression level within that layer. Cortical layers are found at different depths in 2.28 +different parts of the cortex. In preparation for extracting the layer-specific datasets, we have extended Caret with routines 2.29 +that allow the depth of the ROI for volume-to-surface projection to vary. 2.30 In the Research Plan, we describe how we will automatically locate the layer depths. For validation, we have manually 2.31 demarcated the depth of the outer boundary of cortical layer 5 throughout the cortex. 2.32 Using combinations of multiple genes is necessary and sufficient to delineate some cortical areas

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5.1 --- a/grant.txt Mon Apr 13 23:11:04 2009 -0700 5.2 +++ b/grant.txt Mon Apr 13 23:17:40 2009 -0700 5.3 @@ -189,18 +189,18 @@ 5.4 === Flatmap of cortex === 5.5 We downloaded the ABA data and applied a mask to select only those voxels which belong to cerebral cortex. We divided the cortex into hemispheres. 5.6 5.7 -Using Caret\cite{van_essen_integrated_2001}, we created a mesh representation of the surface of the selected region. For each gene, for each node of the mesh, we calculated an average of the gene expression of the voxels "underneath" that mesh node. Using Caret, we then flattened the cortex, creating a two-dimensional mesh. 5.8 +Using Caret\cite{van_essen_integrated_2001}, we created a mesh representation of the surface of the selected region. For each gene, for each node of the mesh, we used Caret to calculate an average of the gene expression of the voxels "underneath" that mesh node. We then used Caret to flatten the cortex, creating a two-dimensional mesh. 5.9 5.10 We sampled the nodes of the irregular, flat mesh in order to create a regular grid of pixel values. We converted this grid into a MATLAB matrix. 5.11 5.12 We manually traced the boundaries of each cortical area from the ABA coronal reference atlas slides. We then converted these manual traces into Caret-format regional boundary data on the mesh surface. Using Caret, we projected the regions onto the 2-d mesh, and then onto the grid, and then we converted the region data into MATLAB format. 5.13 5.14 -At this point, the data is in the form of a number of 2-D matrices, each registered to each other, with the matrix entries representing a grid of points (pixels) over the cortical surface: 5.15 +At this point, the data is in the form of a number of 2-D matrices, all in registration, with the matrix entries representing a grid of points (pixels) over the cortical surface: 5.16 5.17 * A 2-D matrix whose entries represent the regional label associated with each surface pixel 5.18 * For each gene, a 2-D matrix whose entries represent the average expression level underneath each surface pixel 5.19 5.20 -Rather than a single average expression level for each surface pixel, we plan to create a separate matrix for each cortical layer to represent the average expression level within that layer. Cortical layers are found at different depths in different parts of the cortex. In preparation for extracting the layer-specific datasets, we have extended Caret with routines that allow the depth of the ROI for volume-to-surface projection to vary. 5.21 +To move beyond a single average expression level for each surface pixel, we plan to create a separate matrix for each cortical layer to represent the average expression level within that layer. Cortical layers are found at different depths in different parts of the cortex. In preparation for extracting the layer-specific datasets, we have extended Caret with routines that allow the depth of the ROI for volume-to-surface projection to vary. 5.22 5.23 In the Research Plan, we describe how we will automatically locate the layer depths. For validation, we have manually demarcated the depth of the outer boundary of cortical layer 5 throughout the cortex. 5.24 5.25 @@ -217,7 +217,7 @@ 5.26 5.27 Here we give an example of a cortical area which is not marked by any single gene, but which can be identified combinatorially. according to logistic regression, gene wwc1\footnote{"WW, C2 and coiled-coil domain containing 1"; EntrezGene ID 211652} is the best fit single gene for predicting whether or not a pixel on the cortical surface belongs to the motor area (area MO). The upper-left picture in Figure \ref{MOcombo} shows wwc1's spatial expression pattern over the cortex. The lower-right boundary of MO is represented reasonably well by this gene, however the gene overshoots the upper-left boundary. This flattened 2-D representation does not show it, but the area corresponding to the overshoot is the medial surface of the cortex. MO is only found on the lateral surface (todo). 5.28 5.29 -Gnee mtif2\footnote{"mitochondrial translational initiation factor 2"; EntrezGene ID 76784} is shown in figure the upper-right of Fig. \ref{MOcombo}. Mtif2 captures MO's upper-left boundary, but not its lower-right boundary. Mtif2 does not express very much on the medial surface. By adding together the values at each pixel in these two figures, we get the lower-left of Figure \ref{MOcombo}. This combination captures area MO much better than any single gene. 5.30 +Gene mtif2\footnote{"mitochondrial translational initiation factor 2"; EntrezGene ID 76784} is shown in figure the upper-right of Fig. \ref{MOcombo}. Mtif2 captures MO's upper-left boundary, but not its lower-right boundary. Mtif2 does not express very much on the medial surface. By adding together the values at each pixel in these two figures, we get the lower-left of Figure \ref{MOcombo}. This combination captures area MO much better than any single gene. 5.31 5.32 \begin{figure}\label{MOcombo} 5.33 \includegraphics[scale=.36]{MO_vs_Wwc1_jet.eps}