Denoising scanner effects from multimodal MRI data using linked independent component analysis
Denoising scanner effects from multimodal MRI data using linked independent component analysis
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DOI：
10.1016/j.neuroimage.2019.116388
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发表时间：
2020-03-01
期刊：
影响因子：
5.7
通讯作者：
Nickerson, Lisa D.
中科院分区：
文献类型：
--
作者：
Li, Huanjie;Smith, Stephen M.;Nickerson, Lisa D.
跨研究汇集磁共振成像（MRI）数据，或利用来自成像库的共享数据，为推进和增强神经科学研究的可重复性提供了绝佳的机会。然而，扫描仪混淆阻碍了汇集在不同扫描仪上收集的数据或同一扫描仪上的软件和硬件升级，即使所有采集协议都是统一的。这些混淆会降低功效，并可能导致虚假的发现。不幸的是，解决这一问题的方法很少。在这项研究中，我们提出了一种新的去噪方法，该方法实现了数据驱动的链接独立成分分析（LICA），以识别扫描仪相关的影响，从多模态MRI中去除去噪扫描仪的影响。我们利用多项研究数据来测试我们提出的方法，这些数据是在单个3T扫描仪上收集的，软件和主要硬件升级前后以及使用不同的采集参数。我们提出的去噪方法与标准GLM混淆回归或基于ICA的单模态去噪相比，显示出更大的扫描仪相关方差减少。虽然我们没有在这里进行测试，但对于不同扫描仪之间的数据组合，LICA应该证明在识别扫描仪效应方面更好，因为扫描仪之间的差异通常比扫描仪内的差异大得多。我们的方法对于在多研究和大规模多站点研究中消除扫描仪影响具有很大的潜力，这些研究可能会受到扫描仪差异的干扰。
Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.