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

Aurina Arnatkeviciute1, Ben D Fulcher2, Alex Fornito1

  • 1Brain and Mental Health Research Hub, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, 770 Blackburn Rd, Clayton, 3168, VIC, Australia.

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Summary

Researchers explored how processing choices impact findings when combining brain gene expression data with neuroimaging. A unified pipeline is suggested for consistent results in this developing field.

Keywords:
Allen human brain atlasConnectomeGene expressionGeneticsGenomeMRITranscriptome

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Area of Science:

  • Neuroscience
  • Genomics
  • Bioinformatics

Background:

  • Comprehensive brain-wide gene expression atlases, like the Allen Human Brain Atlas (AHBA), enable studying molecular-level spatial variations and macroscopic neuroimaging phenotypes.
  • Existing literature shows links between gene expression and brain structure/function, but methods for integrating expression data with neuroimaging are inconsistent.
  • The impact of these methodological variations on research findings remains unclear.

Purpose of the Study:

  • To outline a standardized seven-step analysis pipeline for integrating brain-wide transcriptomic and neuroimaging data.
  • To compare the influence of different data processing choices on the outcomes of such analyses.
  • To advocate for a unified data processing pipeline in studies utilizing the AHBA for enhanced consistency and reproducibility.

Main Methods:

  • Development of a seven-step analysis pipeline for relating gene expression data from the AHBA with neuroimaging data.
  • Systematic comparison of how variations in data processing steps affect the resulting analyses.
  • Evaluation of the influence of methodological choices on the observed relationships between molecular and macroscopic brain data.

Main Results:

  • Different data processing choices significantly influence the outcomes when combining transcriptomic and neuroimaging data.
  • The study identifies key steps in the pipeline where methodological variations have the most substantial impact.
  • Quantification of the variability introduced by different processing strategies provides insights into current inconsistencies.

Conclusions:

  • Methodological heterogeneity in processing gene expression data poses a challenge for reproducible neuroimaging-gene expression studies.
  • A unified data processing pipeline is crucial for ensuring consistent and reliable findings in this field.
  • Standardization will facilitate more robust discoveries regarding the relationship between molecular profiles and brain phenotypes.