ArticleNature communications2025
Transcriptomic decoding of surface-based imaging phenotypes and its application to pharmacotranscriptomics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Application of the Allen Human Brain Atlas in Alzheimer's disease and Parkinson's disease.Translational neurodegeneration · 2026Review
- Autism and Cortical Thickness Deviation From Neurotypical Controls: Evidence for a Spatial Association With Serotonin Receptors.Autism research : official journal of the International Society for Autism Research · 2026Article
- Decoding the structural and functional diversity of GABAFrontiers in pharmacology · 2025Review
- Multi-modal brain data exploration software for imaging transcriptomics: A comparative evaluation and future directions.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Imaging transcriptomics has become a power tool for linking imaging-derived phenotypes (IDPs) to genomic mechanisms. Yet, its potential for guiding CNS drug discovery remains underexplored. Here, utilizing spatially-dense representations of the human brain transcriptome, we present an analytical framework for the transcriptomic decoding of high-resolution surface-based neuroimaging patterns, and for linking IDPs to the transcriptomic landscape of complex neurotransmission systems in vivo. Leveraging publicly available Positron Emission Tomography (PET) data, we initially validated our approach against molecular targets with a high correspondence between gene expression and protein binding. Subsequently, we used the cortical gene expression profiles of candidate genes to dissect two discrete classes of GABA
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