A practical guide to linking brain-wide gene expression and neuroimaging data

By Aurina Arnatkeviciute, Ben D. Fulcher, Alex Fornito. bioRxiv, 2018

745 citations recorded by the source corpus.

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 the 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 AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/db2de83de4b6f80a44530ca4f7de9585942df920

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• Generative modeling of brain maps with spatial autocorrelation (2020, positive): “…in an adaptive (i.e., target brain-map specific) manner due to its prominence and ubiquity in brain maps (Burt et al., 2018; Markov et al., 2011; ArnatkevicIūtė et al., 2019; Romero-Garcia et al., 2018), its variation across brain maps (Burt et al., 2018; Gryglewski et al., 2018), and because…”