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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.

2025-08-12 neurology
10.1101/2025.08.08.25333301 medRxiv
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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