1.1 --- a/grant.txt Mon Apr 13 20:27:32 2009 -0700 1.2 +++ b/grant.txt Mon Apr 13 23:11:04 2009 -0700 1.3 @@ -119,7 +119,15 @@ 1.4 1.5 Although it is known that different cortical areas have distinct roles in both normal functioning and in disease processes, there are no known marker genes for many cortical areas. When it is necessary to divide a tissue sample into cortical areas, this is a manual process that requires a skilled human to combine multiple visual cues and interpret them in the context of their approximate location upon the cortical surface. 1.6 1.7 -Even the questions of how many areas should be recognized in cortex, and what their arrangement is, are still not completely settled. A proposed division of the cortex into areas is called a cortical map. In the rodent, the lack of a single agreed-upon map can be seen by contrasting the recent maps given by Swanson\cite{brain_swanson_2003} on the one hand, and Paxinos and Franklin\cite{mouse_paxinos_2001} on the other. While the maps are certainly very similar in their general arrangement, significant differences remain in the details. 1.8 +Even the questions of how many areas should be recognized in cortex, and what their arrangement is, are still not completely settled. A proposed division of the cortex into areas is called a cortical map. In the rodent, the lack of a single agreed-upon map can be seen by contrasting the recent maps given by Swanson\cite{swanson_brain_2003} on the one hand, and Paxinos and Franklin\cite{paxinos_mouse_2001} on the other. While the maps are certainly very similar in their general arrangement, significant differences remain in the details. 1.9 + 1.10 +\vspace{0.3cm}**The Allen Mouse Brain Atlas dataset** 1.11 + 1.12 +The Allen Mouse Brain Atlas (ABA) data was produced by doing in-situ hybridization on slices of male, 56-day-old C57BL/6J mouse brains. Pictures were taken of the processed slice, and these pictures were semi-automatically analyzed in order to create a digital measurement of gene expression levels at each location in each slice. Per slice, cellular spatial resolution is achieved. Using this method, a single physical slice can only be used to measure one single gene; many different mouse brains were needed in order to measure the expression of many genes. 1.13 + 1.14 +Next, an automated nonlinear alignment procedure located the 2D data from the various slices in a single 3D coordinate system. In the final 3D coordinate system, voxels are cubes with 200 microns on a side. There are 67x41x58 \= 159,326 voxels in the 3D coordinate system, of which 51,533 are in the brain\cite{ng_anatomic_2009}. 1.15 + 1.16 +Mus musculus, the common house mouse, is thought to contain about 22,000 protein-coding genes\cite{waterston_initial_2002}. The ABA contains data on about 20,000 genes in sagittal sections, out of which over 4,000 genes are also measured in coronal sections. Our dataset is derived from only the coronal subset of the ABA, because the sagittal data does not cover the entire cortex, and has greater registration error\cite{ng_anatomic_2009}. Genes were selected by the Allen Institute for coronal sectioning based on, "classes of known neuroscientific interest... or through post hoc identification of a marked non-ubiquitous expression pattern"\cite{ng_anatomic_2009}. 1.17 1.18 1.19 1.20 @@ -179,17 +187,24 @@ 1.21 1.22 1.23 === Flatmap of cortex === 1.24 -We created a mask which selects only those voxels within the ABA atlas space which belong to cerebral cortex. 1.25 - 1.26 -todo 1.27 - 1.28 -Using Caret, \cite{van_essen_integrated_2001} 1.29 - 1.30 -We manually entered the boundaries of each cortical area into Caret. 1.31 - 1.32 -Cortical layers are found at different depths in different parts of the cortex. We have manually demarcated the depth of the outer boundary of cortical layer 5 throughout the cortex. 1.33 - 1.34 -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. 1.35 +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. 1.36 + 1.37 +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. 1.38 + 1.39 +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. 1.40 + 1.41 +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. 1.42 + 1.43 +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: 1.44 + 1.45 +* A 2-D matrix whose entries represent the regional label associated with each surface pixel 1.46 +* For each gene, a 2-D matrix whose entries represent the average expression level underneath each surface pixel 1.47 + 1.48 +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. 1.49 + 1.50 +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. 1.51 + 1.52 + 1.53 1.54 1.55 1.56 @@ -363,3 +378,4 @@ 1.57 1.58 1.59 1.60 +two hemis