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106: Decoding Brain Maps for Pharmacotranscriptomics
️ Episode 106: Decoding Brain Maps for Pharmacotranscriptomics
In this episode of PaperCast Base by Base, we explore a Nature Communications study, that introduces a fast, surface-based framework to decode imaging-derived brain phenotypes by aligning high-resolution cortical gene-expression maps with PET targets and structural MRI patterns. The work validates gene–protein correspondences in the serotonergic system and then dissects how GABAA_AA receptor subunit expression partitions the cortex into limbic-enriched and broadly cortical systems that relate to anxiety and depression traits.
Study Highlights:
The authors generate vertex-level cortical expression maps from the Allen Human Brain Atlas using geodesic kriging, reduce them to co-expression gradients with spatially autocorrelation-preserving nulls, and compare three decoding strategies—gradient-based vertex-level tests, linear mixed-effects, and generalized least squares—against serotonergic PET atlases. Applying the method to a benzodiazepine-binding PET atlas, they resolve two classes of GABAA_AA subunit genes: Cluster 1 (α2/α3/α5, β1/β3, ε, γ1) with limbic-biased expression and Cluster 2 (α1/α4, β2, γ2/γ3, δ) with widespread cortical expression. They align cortical thickness phenotypes from 279 participants to these signatures and stratify individuals into two neuroanatomical subgroups whose deviations from normative cortical thickness trajectories track with limbic versus widespread GABA signatures. In adults, higher anxiety co-varies with greater cortical-thickness deviations within regions showing high expression of the limbic Cluster 1 signature, supporting a mechanistic bridge between molecular targets and behavior.
Conclusion:
Imaging transcriptomics can function as in vivo pharmacotranscriptomics, suggesting subunit- and pathway-specific targets and stratification markers for precision interventions in neuropsychiatry.
Reference:
Ecker C, Pretzsch C M, Leyhausen J, Berg L M, Gurr C, Seelemeyer H, McAlonan G, Puts N A, Loth E, Dell’Aqua F, Mason L, Charman T, Oakley B, Bourgeron T, Beckmann C, Buitelaar J K, Arango C, Banaschewski T, Chiocchetti A G, Freitag C M, Hattingen E, Krueger-Burg D, Schmeisser M J, Repple J, Reif A & Murphy D G. Transcriptomic decoding of surface-based imaging phenotypes and its application to pharmacotranscriptomics. Nature Communications (2025). https://doi.org/10.1038/s41467-025-61927-3
License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/
Support:
If you'd like to support Base by Base, you can make a one-time or monthly donation here: https://basebybase.castos.com/
On PaperCast Base by Base you’ll discover the latest in genomics, functional genomics, structural genomics, and proteomics.
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By Gustavo Barra️ Episode 106: Decoding Brain Maps for Pharmacotranscriptomics
In this episode of PaperCast Base by Base, we explore a Nature Communications study, that introduces a fast, surface-based framework to decode imaging-derived brain phenotypes by aligning high-resolution cortical gene-expression maps with PET targets and structural MRI patterns. The work validates gene–protein correspondences in the serotonergic system and then dissects how GABAA_AA receptor subunit expression partitions the cortex into limbic-enriched and broadly cortical systems that relate to anxiety and depression traits.
Study Highlights:
The authors generate vertex-level cortical expression maps from the Allen Human Brain Atlas using geodesic kriging, reduce them to co-expression gradients with spatially autocorrelation-preserving nulls, and compare three decoding strategies—gradient-based vertex-level tests, linear mixed-effects, and generalized least squares—against serotonergic PET atlases. Applying the method to a benzodiazepine-binding PET atlas, they resolve two classes of GABAA_AA subunit genes: Cluster 1 (α2/α3/α5, β1/β3, ε, γ1) with limbic-biased expression and Cluster 2 (α1/α4, β2, γ2/γ3, δ) with widespread cortical expression. They align cortical thickness phenotypes from 279 participants to these signatures and stratify individuals into two neuroanatomical subgroups whose deviations from normative cortical thickness trajectories track with limbic versus widespread GABA signatures. In adults, higher anxiety co-varies with greater cortical-thickness deviations within regions showing high expression of the limbic Cluster 1 signature, supporting a mechanistic bridge between molecular targets and behavior.
Conclusion:
Imaging transcriptomics can function as in vivo pharmacotranscriptomics, suggesting subunit- and pathway-specific targets and stratification markers for precision interventions in neuropsychiatry.
Reference:
Ecker C, Pretzsch C M, Leyhausen J, Berg L M, Gurr C, Seelemeyer H, McAlonan G, Puts N A, Loth E, Dell’Aqua F, Mason L, Charman T, Oakley B, Bourgeron T, Beckmann C, Buitelaar J K, Arango C, Banaschewski T, Chiocchetti A G, Freitag C M, Hattingen E, Krueger-Burg D, Schmeisser M J, Repple J, Reif A & Murphy D G. Transcriptomic decoding of surface-based imaging phenotypes and its application to pharmacotranscriptomics. Nature Communications (2025). https://doi.org/10.1038/s41467-025-61927-3
License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/
Support:
If you'd like to support Base by Base, you can make a one-time or monthly donation here: https://basebybase.castos.com/
On PaperCast Base by Base you’ll discover the latest in genomics, functional genomics, structural genomics, and proteomics.