Source-based morphometry analysis of group differences in fractional anisotropy in schizophrenia.
Source-based morphometry analysis of group differences in fractional anisotropy in schizophrenia.
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DOI：
10.1089/brain.2011.0015
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发表时间：
2011
影响因子：
3.4
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Caprihan A;Abbott C;Yamamoto J;Pearlson G;Perrone-Bizzozero N;Sui J;Calhoun VD
出现了基于多元源的形态计量学（SBM）方法，用于处理分数各向异性（FA）数据。有助于给定主题的FA地图。 65岁，18至60岁）和健康对照（n = 102，18至60岁）将显示出类似的额叶和临时组差异模式，与最近的VBM荟萃分析相似。从ICA分析获得的比较和b）比较了从ICA定义的簇获得的加权平均FA值进行组分析。
十个组件中有六个与载荷系数有显着的差异每个ICA空间图的平均FA值通常相对于负载系数具有较大的效应大小。在先前的基于体素的精神分裂症的研究中，SBM确定了几个组成部分，这些成分涵盖了偏连的大脑区域，而多个白质区域则在以前的基于体素的单变量技术中是不可能的。精神分裂症的形态计量学研究。
A multivariate source-based morphometry (SBM) method for processing fractional anisotropy (FA) data is presented. SBM utilizes independent component analysis (ICA) and decomposes an FA image into spatial maps and loading coefficients. The loading coefficients represent the relative degree each component contributes to a given subject’s FA map. We hypothesized that SBM analysis on a large dataset of age- and gender-matched patients with schizophrenia (n = 65, ages 18 to 60 years) and healthy controls (n =102, ages 18 to 60 years) would show a similar, specific pattern of frontal and temporal group differences as a recent VBM meta-analysis. Two approaches using a) the loading coefficients obtained from the ICA analysis, and alternatively b) the weighted mean FA values obtained from the ICA defined clusters were compared for group analysis. Six of the ten selected components had significant group differences with the loading coefficients. Each component was composed of several white matter tracts distributed throughout the brain. Nine of the ten non-artifactual components had significant group differences with the weighted mean FA values. The weighted mean FA values for each ICA spatial map generally had larger effects sizes relative to the loading coefficients. These networks were consistent with regions identified in previous voxel-based studies of schizophrenia. SBM identified several components that covered disjoint brain regions and multiple white matter tracts that would not have been possible with previous voxel-based univariate techniques. Overall, these results suggest the importance of utilizing multivariate approaches in morphometric studies in schizophrenia.