ArticleFrontiers in neuroanatomy2023
Cortical type: a conceptual tool for meaningful biological interpretation of high-throughput gene expression data in the human cerebral cortex.
Article in Frontiers in neuroanatomy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 13 citations in OpenAlex.
- State-dependent signal flow hierarchy in the human cerebral cortex.Nature neuroscience · 2026Article
- Differentiation of cortical areas: effects of free energy minimization with broken symmetry.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Cortical Type: The Basis for a Modern Neurodevelopmental Synthesis (Ontogeny, Phylogeny, Synaptic Connectivity) of the Human Cerebral Cortex.The Journal of comparative neurology · 2026Review
- Cortical circuit principles predict patterns of trauma induced tauopathy in humans.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Structural MRI of brain similarity networks.Nature reviews. Neuroscience · 2025Review
- Multi-neuromeric origin of tyrosine hydroxylase-positive neurons within the substantia nigra and ventral tegmental area.Frontiers in neuroanatomy · 2025Article
- Minimization of prediction errors during cerebral embryogenesis and the emergence of agency.Frontiers in systems neuroscience · 2025Article
- The possibilities of conscious experience in light of the dual origin hypothesis of the neocortex.Neuroscience of consciousness · 2025Article
- Cortical development in the structural model and free energy minimization.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Contracted functional connectivity profiles in autism.Molecular autism · 2024Article
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3 authors at 1 institution in 2 countries.
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Abstract
The interpretation of massive high-throughput gene expression data requires computational and biological analyses to identify statistically and biologically significant differences, respectively. There are abundant sources that describe computational tools for statistical analysis of massive gene expression data but few address data analysis for biological significance. In the present article we exemplify the importance of selecting the proper biological context in the human brain for gene expression data analysis and interpretation. For this purpose, we use cortical type as conceptual tool to make predictions about gene expression in areas of the human temporal cortex. We predict that the expression of genes related to glutamatergic transmission would be higher in areas of simpler cortical type, the expression of genes related to GABAergic transmission would be higher in areas of more complex cortical type, and the expression of genes related to epigenetic regulation would be higher in areas of simpler cortical type. Then, we test these predictions with gene expression data from several regions of the human temporal cortex obtained from the Allen Human Brain Atlas. We find that the expression of several genes shows statistically significant differences in agreement with the predicted gradual expression along the laminar complexity gradient of the human cortex, suggesting that simpler cortical types may have greater glutamatergic excitability and epigenetic turnover compared to more complex types; on the other hand, complex cortical types seem to have greater GABAergic inhibitory control compared to simpler types. Our results show that cortical type is a good predictor of synaptic plasticity, epigenetic turnover, and selective vulnerability in human cortical areas. Thus, cortical type can provide a meaningful context for interpreting high-throughput gene expression data in the human cerebral cortex.
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