Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia
Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia
复制标题
模型阶数对静息态复值 fMRI 数据 ICA 的影响：在精神分裂症中的应用。
DOI：
10.1016/j.jneumeth.2018.02.013
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发表时间：
2018
影响因子：
3
通讯作者：
Calhoun Vince D.
中科院分区：
文献类型：
--
作者：
Kuang Li-Dan;Lin Qiu-Hua;Gong Xiao-Feng;Cong Fengyu;Sui Jing;Calhoun Vince D.
在功能磁共振成像（fMRI）数据的独立成分分析（伊卡）中，在较高的模型阶数下进行成分分裂是一个被广泛接受的发现。然而，我们最近的研究发现，完整的组件发生与子组件在较高的modelorders.New methodThis研究探讨了模型顺序的影响伊卡的静息态复值fMRI数据从82名受试者，其中包括40名健康对照（HC）和42名精神分裂症患者。此外，我们探讨了根本原因之间的复杂值的数据和幅度只有数据的ICA相位功能磁共振成像数据的模型顺序的影响，不同的组件分裂。提出了一种结合受试者平均值和单样本检验的联合收割机最佳运行选择方法。我们选择默认模式网络（DMN），视觉和感觉运动相关的组件从伊卡的最佳运行在不同的模型阶数从10到140。结果显示，组件集成发生在复值和相位分析，而组件分裂出现在幅度分析随着模型阶数的增加。相位数据的纳入似乎在保持大脑网络的完整性方面起着补充作用。与现有方法的比较与仅幅度分析相比，在更高的模型阶数下，在复值分析中获得的完整DMN分量在HC和精神分裂症患者之间表现出非常显著的受试者水平差异。我们检测到显着更高的活动和变化，在前面的地区为HC和在后方地区的精神分裂症patients.ConclusionsThese结果表明，潜在的复值功能磁共振成像数据的贡献一般和具体的脑网络分析，在识别精神分裂症相关的变化。
BackgroundComponent splitting at higher model orders is a widely accepted finding for independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. However, our recent study found that intact components occurred with subcomponents at higher model orders.New methodThis study investigated model order effects on ICA of resting-state complex-valued fMRI data from 82 subjects, which included 40 healthy controls (HCs) and 42 schizophrenia patients. In addition, we explored underlying causes for distinct component splitting between complex-valued data and magnitude-only data by examining model order effects on ICA of phase fMRI data. A best run selection method was proposed to combine subject averaging and a one-samplet-test. We selected the default mode network (DMN)-, visual-, and sensorimotor-related components from the best run of ICA at varying model orders from 10 to 140.ResultsResults show that component integration occurred in complex-valued and phase analyses, whereas component splitting emerged in magnitude-only analysis with increasing model order. Incorporation of phase data appears to play a complementary role in preserving integrity of brain networks.Comparison with existing method(s)When compared with magnitude-only analysis, the intact DMN component obtained in complex-valued analysis at higher model orders exhibited highly significant subject-level differences between HCs and patients with schizophrenia. We detected significantly higher activity and variation in anterior areas for HCs and in posterior areas for patients with schizophrenia.ConclusionsThese results demonstrate the potential of complex-valued fMRI data to contribute generally and specifically to brain network analysis in identification of schizophrenia-related changes.