Neudorfer Clemens, Al-Fatly Bassam, Hollunder Barbara, Li Ningfei, Meyer Garance M, Rajamani Nanditha, Butenko Konstantin, Vissani Matteo, Bush Alan, Sisterson Nathaniel D, Tadayon Ehsan, Schaper Frederic, Bahners Bahne, Hart Lauren, Madan Savir, Pijar Julianna, Mosley Philip, Akram Harith, Acevedo Nicola, Castle David, Rossell Susan, Bosanac Peter, Ostrem Jill L, Starr Philip A, Odekerken Vincent Jj, deBie Rob Ma, Barcia Juan A, Tyagi Himanshu, Sheth Sameer A, Goodman Wayne K, Visser-Vandewalle Veerle, Figee Martijn, Dougherty Darin D, Zrinzo Ludvic, Joyce Eileen, Corp Daniel, Joutsa Juho, Picht Thomas, Faust Katharina, Kühn Andrea A, Ganos Christos, Scharf Jeremiah, Klein Christine, Fox Michael D, Richardson R Mark, Horn Andreas
Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
medRxiv. 2025 Aug 12:2025.08.08.25333301. doi: 10.1101/2025.08.08.25333301.
Gene expression shapes the brain's 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 -network maps, we captured distributed connectivity patterns for individual genes. Aggregating these across genes implicated in the same disorder yielded -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.
基因表达塑造了大脑的功能连接组，但尚不清楚与同一疾病相关的基因是否汇聚在共享网络上。我们引入了基因网络映射——一种将空间转录组学与规范功能连接相结合的框架，以识别与基因表达相关的网络。通过生成网络图谱，我们捕获了单个基因的分布式连接模式。对涉及同一疾病的这些基因进行汇总，得到了网络图谱，该图谱捕获了对脑网络的累积遗传影响。我们通过将这些图谱与病变衍生网络进行比较，并测试这些网络的调节是否能预测深部脑刺激（DBS）队列中的结果，来验证这些图谱。该框架为研究脑部疾病的分子结构提供了一种新工具，并支持精准医学中基于网络的诊断和治疗。
