Integrative Neuroinformatics for Neuroanatomy

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Decoding the human genome has allowed us to discover associations between specific genes and diseases. It has also enabled us to measure expression or usage of all genes in the brain. The brain is the most complex object known to man, this complexity is seen in differences in gene usage in the brain, with three-quarters of genes changing across brain regions or age. While many genes are known to directly affect brain structure, many genes used in the brain play unknown roles. This research seeks to find patterns of gene usage that can help understand three other measures of the human brain. The first is neuroanatomy, we seek to find genes that differ across different regions and layers of the brain by combining several large datasets of gene usage. Second, we will join gene usage information with the grayscale images produced from magnetic resonance scans (MRI). Lastly, we examine epigenetic information that influences gene usage by marking the genome with specific tags. We will test if the relationships between gene usage and these methylation tags are different across time or sex. This work aims to increase our understanding of the molecules underlying neuroimages, brain structure, and control of gene usage. These projects will be undertaken by students that will learn how to apply data science methods to handle the large and complex datasets.