Prediction of a cell-type specific mouse mesoconnectome using gene expression data

Abstract

Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-type specificity would be a major step forward. We analysed the ability of gene expression patterns to predict cell-type and laminar specific projection patterns and analyzed the biological context of the most predictive groups of genes. To achieve our goal, we used publicly available volumetric gene expression and connectivity data and pre-processed it for prediction by averaging across brain regions, imputing missing values and rescaling. Afterwards, we predicted the strength of axonal projections and their binary form using expression patterns of individual genes and co-expression patterns of spatial gene modules. For predicting projection strength, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r 2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using the dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r 2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to post-

Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

“…This points out the necessity of conducting posthoc regression analyses for identifying the most optimal mixtures of dictionaries. In Ji et al (2014) and Timonidis et al (2020), predictions of projection patterns as sparse linear combinations of gene expression patterns were shown to be significant when representing both modalities at the level of brain areas. However, Linked ICA provides an advantage in terms of interpretation, since reconstructing both data modalities is implicitly modelled by the method instead of requiring post-hoc analyses.…”

“…Additionally, the meta-analytic steps of our analysis have been designed and tested in the form of a Jupyter Notebook and have been published online with their descriptions at Github. The Github Notebook has been incorporated in the Connectomic-Composition-Predictor (CCP), a Neuroinformatics-related tool that we developed in our previous work (Timonidis et al 2020). Regarding the modifications made to Linked ICA in this work, a potential user can consult the steps described in Supplementary Material Section 1.1.…”