Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets.
Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets.
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DOI：
10.1016/j.neuroimage.2021.118189
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发表时间：
2021-08-15
期刊：
影响因子：
5.7
通讯作者：
Griffanti L
中科院分区：
文献类型：
--
作者：
Bordin V;Bertani I;Mattioli I;Sundaresan V;McCarthy P;Suri S;Zsoldos E;Filippini N;Mahmood A;Melazzini L;Laganà MM;Zamboni G;Singh-Manoux A;Kivimäki M;Ebmeier KP;Baselli G;Jenkinson M;Mackay CE;Duff EP;Griffanti L
我们协调了两项关于健康老龄化的研究中的WMH指标。具体的预处理策略可以增加研究之间的可比性。生物差异的建模对于提供校准措施至关重要。大规模的神经影像学数据集提供了为各种神经影像学标记物提供规范分布的可能性，这将大大提高这些措施的临床效用。然而，一个主要的挑战是，由于不同协议和人群中成像和非成像测量的不一致性，我们目前在不同大规模数据集上整合测量的能力很差。在这里，我们探讨了协调白色高信号（WMH）措施在两个主要的研究健康老年人群，白厅II成像子研究和英国生物银行。我们确定了预处理策略，最大限度地提高了数据集之间的一致性，并利用多变量回归分析研究样本差异，这些差异导致了研究中WMH变化的差异。我们还提出了一个解析器来协调两个数据集的WMH相关非成像变量。我们表明，我们可以从这些数据集中提供高度校准的WMH测量：（1）包括一些特定的标准化处理步骤;（2）通过调整人口统计学，认知和生理变量对样本差异进行适当建模。这些结果为WMH和其他神经影像学标记物在广泛的临床数据库中的研究开辟了广泛的应用。
We harmonised measures of WMHs across two studies on healthy ageing. Specific pre-processing strategies can increase comparability across studies. Modelling of biological differences is crucial to provide calibrated measures. Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise study sample differences contributing to differences in WMH variations across studies. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.
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