ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI.
ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI.
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DOI：
10.3389/fnins.2015.00395
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发表时间：
2015
影响因子：
4.3
通讯作者：
Mackay CE
中科院分区：
文献类型：
--
作者：
Feis RA;Smith SM;Filippini N;Douaud G;Dopper EG;Heise V;Trachtenberg AJ;van Swieten JC;van Buchem MA;Rombouts SA;Mackay CE
静息态功能磁共振成像（R-fMRI）在为一系列疾病的诊断、预后和药物反应提供潜在的生物标志物方面显示出相当大的前景。将R-fMRI应用于多中心研究正变得越来越流行，这给数据采集和分析带来了技术挑战，因为fMRI数据对硬件、软件和环境差异造成的结构化噪声特别敏感。在这里，我们研究了一种新的结构化噪声清理工具是否能够减少健康受试者之间的中心相关R-fMRI差异。我们分析了来自72名受试者的3个Tesla R-fMRI数据，其中一半在荷兰的Philips Achieva系统中闭眼扫描，另一半在英国的Siemens Trio系统中睁眼扫描。在预统计处理和个体独立成分分析（伊卡）之后，使用FMRIB的基于ICA的X噪声器（FIX）从数据中去除噪声成分。运行GICA和二元回归，并使用非参数统计量比较应用FIX前后组间的空间图。在使用FIX前，研究中心之间的所有静息状态网络均存在较大的显著性差异，其中大部分在应用FIX后降低至不显著。中间/主要视觉网络的中心间差异（可能反映了方案中的中心间差异）仍具有统计学显著性。FIX通过减少R-fMRI数据中的结构化噪声，有助于促进多中心R-fMRI研究。在这样做的过程中，它改进了来自新环境中不同中心的现有数据的组合，以及对罕见疾病和风险基因的比较，其中足够的样本量仍然是一个挑战。
Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects. We analyzed three Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX. Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant. FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge.