Gupta Cota Navin, Castro Eduardo, Rachkonda Srinivas, van Erp Theo G M, Potkin Steven, Ford Judith M, Mathalon Daniel, Lee Hyo Jong, Mueller Bryon A, Greve Douglas N, Andreassen Ole A, Agartz Ingrid, Mayer Andrew R, Stephen Julia, Jung Rex E, Bustillo Juan, Calhoun Vince D, Turner Jessica A
The Mind Research Network, Albuquerque, NM, United States.
Department of Biosciences and Bioengineering, Indian Institute of Technology, Guwahati, India.
Front Psychiatry. 2017 Sep 26;8:179. doi: 10.3389/fpsyt.2017.00179. eCollection 2017.
Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms ( = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component ( = 0.25; = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development.
精神分裂症（Sz）中基于临床和认知症状领域的亚型分类因缺乏神经生物学相关性以及症状评分的异质性而受到批评。因此，我们提出了一种新的数据驱动框架，使用双聚类独立成分分析从Sz患者可靠且稳定的灰质浓度（GMC）中检测亚型。所开发的方法包括以下步骤：基于源的形态计量学（SBM）分解、两个成分负荷的选择和排序、使用组信息引导的独立成分分析（GIG-ICA）进行亚型成分重建。该框架应用于我们之前SBM研究中前两个组判别成分，即岛叶/颞上回/额下回（I-STG-IFG成分）和额上回/额中回/额内侧回（SFG-MiFG-MFG成分），这两个成分显示出诊断组差异且具有最高的效应量。汇总的多站点数据集包括382例Sz患者，按年龄、性别和体素进行了回归分析。我们观察到两个亚型（即两个不同的受试者子集），分别在这两个成分上有很大权重。这些受试者子集的特征是阳性和阴性综合征量表（PANSS）阳性临床症状存在显著差异（=0.005）。我们还观察到一个在这两个成分上都有很大权重的重叠亚型。该亚型的PANSS一般临床症状与SFG-MiFG-MFG成分的负荷系数呈趋势水平相关（=0.25；=0.07）。与组成分相比，使用GIG-ICA重建的亚型特异性成分在体素区域显示出差异。我们观察到GMC均值的偏差以及表征每个破译亚型的两个成分特征的结合。Sz患者中GMC的这些固有差异可能表明需要个性化治疗和靶向药物开发。
