SynthesisBMC medical genomics2022
Gene co-expression changes underlying the functional connectomic alterations in Alzheimer's disease.
Synthesis in BMC medical genomics, 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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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
- Identification ofFrontiers in aging neuroscience · 2026Article
- Mining single-cell data for cell type-disease associations.NAR genomics and bioinformatics · 2024Article
- Neurogenetics of Brain Connectivity: Current Approaches to the Study (Review).Sovremennye tekhnologii v meditsine · 2024Review
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5 authors.
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Abstract
backgroundThere is growing evidence indicating that a number of functional connectivity networks are disrupted at each stage of the full clinical Alzheimer's disease spectrum. Such differences are also detectable in cognitive normal (CN) carrying mutations of AD risk genes, suggesting a substantial relationship between genetics and AD-altered functional brain networks. However, direct genetic effect on functional connectivity networks has not been measured.
methodsLeveraging existing AD functional connectivity studies collected in NeuroSynth, we performed a meta-analysis to identify two sets of brain regions: ones with altered functional connectivity in resting state network and ones without. Then with the brain-wide gene expression data in the Allen Human Brain Atlas, we applied a new biclustering method to identify a set of genes with differential co-expression patterns between these two set of brain regions.
resultsDifferential co-expression analysis using biclustering method led to a subset of 38 genes which showed distinctive co-expression patterns between AD-related and non AD-related brain regions in default mode network. More specifically, we observed 4 sub-clusters with noticeable co-expression difference, where the difference in correlations is above 0.5 on average.
conclusionsThis work applies a new biclustering method to search for a subset of genes with altered co-expression patterns in AD-related default mode network regions. Compared with traditional differential expression analysis, differential co-expression analysis yielded many more significant hits with extra insights into the wiring mechanism between genes. Particularly, the differential co-expression pattern was observed between two sets of genes, suggesting potential upstream genetic regulators in AD development.
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