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Bioinformatics for neuroanatomical connectivity

University of British Columbia

Neuroscience research is increasingly dependent on bringing together large amounts of data collected at the molecular, anatomical, functional and behavioural levels. This data is disseminated in scientific articles and large online databases. I utilized these large resources to study the wiring diagram of the brain or ‘connectome’. The aims of this thesis were to automatically collect large amounts of connectivity knowledge and to characterize relationships between connectivity and gene expression in the rodent brain. To extract the knowledge embedded in the neuroscience literature I created the first corpus of neuroscience abstracts annotated for brain regions and their connections. These connections describe long distance or macroconnectivity between brain regions. The collection of over 1,300 abstracts allowed accurate training of machine learning classifiers that mark brain region mentions (76% recall at 81% precision) and neuroanatomical connections between regions (50% sentence level recall at 70% precision). By automatically extracting connectivity statements from the Journal of Comparative Neurology I generated a literature based connectome of over 28,000 connections. Evaluations revealed that a large number of brain region descriptions are not found in existing lexicons. To address this challenge I developed novel methods that allow mapping of brain region terms to enclosing structures. To further study the connectome I moved from scientific articles to large online databases. By employing resources for gene expression and connectivity I showed that patterns of gene expression correlate with connectivity. First, two spatially anti-correlated patterns of mouse brain gene expression were identified. These signatures are associated with differences in expression of neuronal and oligodendrocyte markers, suggesting they reflect regional differences in cellular populations. Expression level of these genes is correlated with connectivity degree, with regions expressing the neuron-enriched pattern having more incoming and outgoing connections with other regions. Finally, relationships between profiles of gene expression and connectivity were tested. Specifically, I showed that brain regions with similar expression profiles tend to have similar connectivity profiles. Further, optimized sets of connectivity linked genes are associated with neuronal development, axon guidance and autistic spectrum disorder. This demonstration of text mining and large scale analysis provides new foundations for neuroinformatics.

Text

2012-01-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/40369

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/