Gupta Cota Navin, Calhoun Vince D, Rachakonda Srinivas, Chen Jiayu, Patel Veena, Liu Jingyu, Segall Judith, Franke Barbara, Zwiers Marcel P, Arias-Vasquez Alejandro, Buitelaar Jan, Fisher Simon E, Fernandez Guillen, van Erp Theo G M, Potkin Steven, Ford Judith, Mathalon Daniel, McEwen Sarah, Lee Hyo Jong, Mueller Bryon A, Greve Douglas N, Andreassen Ole, Agartz Ingrid, Gollub Randy L, Sponheim Scott R, Ehrlich Stefan, Wang Lei, Pearlson Godfrey, Glahn David C, Sprooten Emma, Mayer Andrew R, Stephen Julia, Jung Rex E, Canive Jose, Bustillo Juan, Turner Jessica A
The Mind Research Network, Albuquerque, NM;
The Mind Research Network, Albuquerque, NM; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM;
Schizophr Bull. 2015 Sep;41(5):1133-42. doi: 10.1093/schbul/sbu177. Epub 2014 Dec 28.
Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both source-based morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects.
对精神分裂症（Sz）患者灰质浓度（GMC）缺陷的分析已确定整个皮质存在显著变化。我们在迄今为止最大的聚合结构成像数据集中评估了诊断、总体症状严重程度与灰质模式之间的关系。我们对来自欧洲和美国23个扫描点的784名精神分裂症患者和936名对照者（Ct）的GMC图像进行了基于源的形态计量学（SBM）和基于体素的形态计量学（VBM）分析。在对年龄、性别、扫描点以及扫描点与诊断之间的交互作用进行校正后，SBM分析显示出9种诊断差异模式。它们包括不同的皮质、皮质下和小脑区域。7种模式显示对照者的GMC高于精神分裂症患者，而2种模式（脑干和小脑）显示精神分裂症患者的GMC更高。最大的GMC缺陷存在于一种单一模式中，该模式包括颞上回、额下回和额内侧皮质的区域，在对数据子集的分析中得到了重复。VBM分析确定了精神分裂症患者总体皮质GMC减少以及一小簇GMC增加，这与SBM脑干成分重叠。在两种分析中，我们均未发现成分负荷与症状严重程度之间存在显著关联。这项大型分析证实，即使在如此多样的数据集中，精神分裂症患者前颞叶、岛叶和额内侧叶中常见的GMC减少也形成了一种单一、一致的空间模式。将GMC减少分离为跨多个数据集的稳健、可重复的空间模式，为应用这些方法识别细微的遗传和临床队列效应铺平了道路。
