Since the translation of data between species remains subjective, our goal is to develop a data-driven tool to perform this translation based on the expression patterns of homologous genes. For this, human and mouse fMRI data, which was manually matched based on spatial homology between co-activation patterns, was modelled as a linear addition of gene expression maps. Weighting factors were then used to estimate synthetic brain states based on homologous genes. When comparing the synthetic brain states to their matched biological state, they showed higher correlation than compared to mismatched states, showing the effectiveness of the translational tool.
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