School of Biomedical Engineering, Dalian University of Technology, Dalian, China; McLean Imaging Center, McLean Hospital, Belmont, MA, United States; Department of Psychiatry, Harvard Medical School, Boston, MA, United States.
FMRIB (Oxford University Centre for Functional MRI of the Brain), Department Clinical Neurology, University of Oxford, UK.
Neuroimage. 2020 Mar;208:116388. doi: 10.1016/j.neuroimage.2019.116388. Epub 2019 Nov 23.
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.
跨研究汇集磁共振成像 (MRI) 数据，或利用成像存储库中的共享数据，为推进和增强神经科学研究的可重复性提供了极好的机会。然而，扫描仪混淆因素阻碍了在不同扫描仪上或在同一扫描仪上的软件和硬件升级上收集的数据的汇集，即使所有采集协议都得到了协调。这些混淆因素降低了功效，并可能导致虚假发现。不幸的是，解决这个问题的方法很少。在这项研究中，我们提出了一种新颖的去噪方法，该方法实现了数据驱动的链接独立成分分析 (LICA)，以识别与扫描仪相关的影响，以便从多模态 MRI 中去除，从而对扫描仪效应进行去噪。我们利用多研究数据来测试我们的方法，这些数据是在单个 3T 扫描仪上收集的，在软件和主要硬件升级前后，并使用不同的采集参数。与标准 GLM 混杂回归或基于 ICA 的单模态去噪相比，我们提出的去噪方法显示出更大的扫描仪相关方差减少。虽然我们在这里没有进行测试，但对于跨不同扫描仪的数据组合，LICA 应该更能识别扫描仪效应，因为扫描仪之间的变异性通常比扫描仪内的变异性大得多。我们的方法在多研究和大型多站点研究中具有很大的潜力，可以消除扫描仪差异造成的混杂。
