Null models for imaging transcriptomics

COEX 

The emergence of brain-wide transcriptional atlases, quantifying the expression of thousands of genes across multiple locations in the brain, opened new opportunities for investigating the molecular correlates of brain network organization. Spatial gene expression data are highly multidimensional, therefore, evaluating associations with neuroimaging measures requires additional considerations across multiple levels of analysis. In this talk I will briefly introduce general concepts of imaging transcriptomics analyses and then will focus on the three main areas for applying different null models: i) dealing with spatial autocorrelation when evaluating the associations between gene expression and neuroimaging data; ii) assessing the specificity of identified associations for the selected sets of genes; and iii) evaluating the functional implications through gene enrichment analyses. I will conclude by outlining the recommendations for best practices and providing an overview of freely available open-source toolboxes implementing current best-practice procedures.