Translating the Transcriptome: A Connectomics Approach for Gene-Network Mapping and Clinical Application
Neudorfer, C.; Al-Fatly, B.; Hollunder, B.; Li, N.; Meyer, G. M.; Rajamani, N.; Butenko, K.; Vissani, M.; Bush, A.; Sisterson, N.; Tadayon, E.; Schaper, F.; Pijar, J.; Bahners, B.; Hart, L.; Madan, S.; Mosley, P.; Akram, H.; Acevedo, N.; Castle, D.; Rossell, S.; Bosanac, P.; Ostrem, J.; Starr, P.; Odekerken, V.; deBie, R.; Barcia, J.; Tyagi, H.; Sheth, S.; Goodman, W.; Figee, M.; Dougherty, D.; Visser-Vandewalle, V.; Zrinzo, L.; Joyce, E.; Corp, D.; Joutsa, J.; Picht, T.; Faust, K.; Kuehn, A.; Ganos, C.; Scharf, J.; Klein, C.; Fox, M. D.; Richardson, M.; Horn, A.
Show abstract
Gene expression shapes the brains functional connectome, yet it is unclear whether genes linked to the same disorder converge on shared networks. We introduce gene network mapping-a framework combining spatial transcriptomics with normative functional connectivity to identify networks associated with gene expression. By generating gene-network maps, we captured distributed connectivity patterns for individual genes. Aggregating these across genes implicated in the same disorder yielded disease-network maps that captured the cumulative genetic impact on brain networks. We validated these maps by comparing them to lesion-derived networks and testing whether modulation of these networks predicted outcomes in deep brain stimulation (DBS) cohorts. This framework offers a novel tool to study the molecular architecture of brain disorders and supports the network-informed diagnostics and therapeutics in precision medicine.
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