Neuroimaging and multiomics reveal cross-scale circuit abnormalities in schizophrenia
Meng Wang, Hao Yan, Xiaohan Tian, Weihua Yue, Yong Liu, Lingzhong Fan, Ke Hu, Yuqing Sun, Yuxin Zhao, Jing Lou, Ming Song, Peng Li, Jun Chen, Yunchun Chen, Huaning Wang, Wenming Liu, Zhigang Li, Yongfeng Yang, Hua Guo, Luxian Lv, Jun Yan, Huiling Wang, Hongxing Zhang, Huawang Wu, Yuping Ning, Lin Lu, Dai Zhang, Ang Li, Tianzi Jiang & Bing Liu
Abstract
Schizophrenia (SCZ) is a highly heterogeneous disorder with diverse clinical manifestations and macro- and microscale biological variations, usually observed at dissociable levels. Here we propose a cross-scale, circuit-based framework to connect heterogeneous clinical symptoms, large-scale brain circuit dysfunctions, and genetic, molecular and cellular abnormalities in SCZ. Using connectomic and predictive models on three independent neuroimaging datasets (n = 1,199, including patients with SCZ and healthy controls), we first identified two macroscale dysconnectivity dimensions for corticocortical and corticostriatal circuits, each associated with specific clinical symptoms. We then associated macroscale dysconnectivity with disrupted cellular circuits using extended imaging transcriptomic and genetic analyses on multiomics data. Our findings suggest a two-dimensional cross-scale heterogeneity model of SCZ, which reveals how distinct genetic disruptions affect specific cellular-level deficits, resulting in system-level brain circuit dysconnectivity responsible for the heterogeneous symptoms in SCZ. These findings significantly improve our understanding of cross-scale heterogeneity in SCZ, advancing its pathophysiology and treatment development.
附件下载：
Neuroimaging and multiomics reveal cross-scale circuit abnormalities in schizophrenia, Nat Mental Health, 28 Aug 2023
Nature Mental Health, 28 August, 2023, DOI：https://doi.org/10.1038/s44220-023-00110-3
Neuroimaging and multiomics reveal cross-scale circuit abnormalities in schizophrenia
Meng Wang, Hao Yan, Xiaohan Tian, Weihua Yue, Yong Liu, Lingzhong Fan, Ke Hu, Yuqing Sun, Yuxin Zhao, Jing Lou, Ming Song, Peng Li, Jun Chen, Yunchun Chen, Huaning Wang, Wenming Liu, Zhigang Li, Yongfeng Yang, Hua Guo, Luxian Lv, Jun Yan, Huiling Wang, Hongxing Zhang, Huawang Wu, Yuping Ning, Lin Lu, Dai Zhang, Ang Li, Tianzi Jiang & Bing Liu
Abstract
Schizophrenia (SCZ) is a highly heterogeneous disorder with diverse clinical manifestations and macro- and microscale biological variations, usually observed at dissociable levels. Here we propose a cross-scale, circuit-based framework to connect heterogeneous clinical symptoms, large-scale brain circuit dysfunctions, and genetic, molecular and cellular abnormalities in SCZ. Using connectomic and predictive models on three independent neuroimaging datasets (n = 1,199, including patients with SCZ and healthy controls), we first identified two macroscale dysconnectivity dimensions for corticocortical and corticostriatal circuits, each associated with specific clinical symptoms. We then associated macroscale dysconnectivity with disrupted cellular circuits using extended imaging transcriptomic and genetic analyses on multiomics data. Our findings suggest a two-dimensional cross-scale heterogeneity model of SCZ, which reveals how distinct genetic disruptions affect specific cellular-level deficits, resulting in system-level brain circuit dysconnectivity responsible for the heterogeneous symptoms in SCZ. These findings significantly improve our understanding of cross-scale heterogeneity in SCZ, advancing its pathophysiology and treatment development.
文章链接：https://www.nature.com/articles/s44220-023-00110-3
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