In a study conceptually similar to another one I mentioned a few months ago, Wolf et al discuss their results from comparing and contrasting USC’s rat brain connectivity atlas with the Allen mouse brain gene expression atlas.
To combine these, they first had to map the regions from the rat brain to the mouse brain.
The authors then trained a linear classifier to try to predict whether two given brain regions have a connection or not, using vectors of gene expression of the 500 most predictive genes in both incoming and outgoing connections as features. They use 80% training and 20% testing cross-validation. Finally, they randomly shuffle the brain regions and re-do the analyses to compute empirical p-values.
The Allen brain atlas is hierarchical, which is useful for some analyses but could lead to double counting of brain regions here. So the authors only analyze the brain regions at the lowest level of the hierarchy–the outermost nodes in the circular diagram below.
Only brain regions with more than 5 outgoing or incoming connections were analyzed. They found that connectivity was able to be predicted by the gene expression in many regions. See below for details:
They also analyzed genes that are thought to be involved in brain disorders like schizophrenia. Schizophrenia-related genes are much more likely to be involved in connectivity patterns that would be expected due to chance, bolstering the hypothesis that this disorder is related to neural connectivity. Defining the null hypothesis here seems a bit tricky though, so we’ll need a wide breadth of studies to help confirm this finding.
It would be interesting to see if the predictive ability of gene expression is higher using the developing mouse brain atlas as opposed to the adult. This is expected because that’s when most long-range axons form, but there is also lots of dendrite rearrangment and plasticity in adults, too.
Wolf L, Goldberg C, Manor N, Sharan R, Ruppin E (2011) Gene Expression in the Rodent Brain is Associated with Its Regional Connectivity. PLoS Comput Biol 7(5): e1002040. doi:10.1371/journal.pcbi.1002040