ArticleHuman brain mapping2022
Regional gene expression patterns are associated with task-specific brain activation during reward and emotion processing measured with functional MRI.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- Computational reconstruction of evolutionary selection in human brain networks.Frontiers in neuroinformatics · 2025Article
- Regional gene expression patterns are associated with task-specific brain activation during reward and emotion processing measured with functional MRI.Human brain mapping · 2022Article
- Multi-modal brain data exploration software for imaging transcriptomics: A comparative evaluation and future directions.Imaging neuroscience (Cambridge, Mass.)Article
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9 authors.
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Abstract
The exploration of the spatial relationship between gene expression profiles and task-evoked response patterns known to be altered in neuropsychiatric disorders, for example depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in cellular localization processes, metabolism, translation, and synapse regulation. An overlap of these genes with risk genes from major depressive disorder genome-wide association studies revealed the involvement of the master regulators TCF4 and PAX6 in emotion and reward processing. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.
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