Integrative analysis of the connectivity and gene expression atlases in the mouse brain

Brain function is the result of interneuron signal transmission controlled by the fundamental biochemistry of each neuron. The biochemical content of a neuron is in turn determined by spatiotemporal gene expression and regulation encoded into the genomic regulatory networks. It is thus of particular interest to elucidate the relationship between gene expression patterns and connectivity in the brain. However, systematic studies of this relationship in a single mammalian brain are lacking to date. Here, we investigate this relationship in the mouse brain using the Allen Brain Atlas data. We employ computational models for predicting brain connectivity from gene expression data. In addition to giving competitive predictive performance, these models can rank the genes according to their predictive power. We show that gene expression is predictive of connectivity in the mouse brain when the connectivity signals are discretized. When the expression patterns of 4084 genes are used, we obtain a predictive accuracy of 93%. Our results also show that a small number of genes can almost give the full predictive power of using thousands of genes. We can achieve a prediction accuracy of 91% by using only 25 genes. Gene ontology analysis of the highly ranked genes shows that they are enriched for connectivity related processes.

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