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

• Published in: NeuroImage (Orlando, Fla.) Vol. 189; pp. 353 - 367
• Main Authors: Arnatkevic̆iūtė, Aurina, Fulcher, Ben D., Fornito, Alex
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2019; Elsevier Limited
• Subjects: Adult; Allen human brain atlas; Atlases as Topic; Brain - anatomy & histology; Brain - metabolism; Brain research; Connectome; Data processing; Deoxyribonucleic acid; DNA; Female; Functional anatomy; Gene Expression; Genetics; Genome; Genomes; Humans; Hypotheses; Magnetic Resonance Imaging; Male; Medical imaging; Microarray Analysis; Middle Aged; MRI; Neuroimaging; Neuroimaging - methods; Phenotypes; Software; Spatial variations; Structure-function relationships; Transcriptome; Young Adult; Allen human brain atlas; MRI; Transcriptome; Genetics; Connectome; Gene expression; Genome
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.01.011

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. [Display omitted]