Unveiling the neuroimaging-genetic intersections in the human brain.

Area of Science:

• Neuroscience
• Genomics
• Bioinformatics

Background:

• The availability of large-scale, open-access brain-wide and single-cell transcriptome datasets presents novel opportunities.
• Integrating neuroimaging findings with genetic data is crucial for advancing our understanding of brain function and disease.

Purpose of the Study:

• To review and present methodological approaches for combining neuroimaging and transcriptome data.
• To highlight the potential of integrated data analysis in neuroscience research.

Main Methods:

• The study reviews existing literature that integrates neuroimaging and gene expression data.
• Focuses on diverse methodological strategies employed in combining these datasets.

Main Results:

• Studies have successfully correlated neuroimaging maps with the spatial distribution of gene expression.
• Integration has led to functional gene annotations, identification of cell types linked to phenotypes, and insights into gene expression across the lifespan.
• The importance of specific genes in disease genetic networks has been elucidated.

Conclusions:

• New transcriptome datasets and analytical methods enhance current neuroimaging research.
• Combining these data types is vital for a deeper understanding of the biological mechanisms underlying neurological disorders.