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Abstract
The recent availability of comprehensive, brain-wide gene expression atlases such
as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding
how spatial variations on molecular scale relate to the macroscopic neuroimaging phenotypes.
A rapidly growing body of literature is demonstrating relationships between gene expression
and diverse properties of brain structure and function, but approaches for combining
expression atlas data with neuroimaging are highly inconsistent, with substantial
variations in how the expression data are processed. The degree to which these methodological
variations affect findings is unclear. Here, we outline a seven-step analysis pipeline
for relating brain-wide transcriptomic and neuroimaging data and compare how different
processing choices influence the resulting data. We suggest that studies using the
AHBA should work towards a unified data processing pipeline to ensure consistent and
reproducible results in this burgeoning field.