Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
Department of Statistics, University of Warwick, Coventry CV4 7AL, UK; The Alan Turing Institute, London NW1 2DB, UK.
Neuroimage. 2021 Aug 1;236:118090. doi: 10.1016/j.neuroimage.2021.118090. Epub 2021 Apr 22.
White matter lesions are a very common finding on MRI in older adults and their presence increases the risk of stroke and dementia. Accurate and computationally efficient modelling methods are necessary to map the association of lesion incidence with risk factors, such as hypertension. However, there is no consensus in the brain mapping literature whether a voxel-wise modelling approach is better for binary lesion data than a more computationally intensive spatial modelling approach that accounts for voxel dependence.
We review three regression approaches for modelling binary lesion masks including mass-univariate probit regression modelling with either maximum likelihood estimates, or mean bias-reduced estimates, and spatial Bayesian modelling, where the regression coefficients have a conditional autoregressive model prior to account for local spatial dependence. We design a novel simulation framework of artificial lesion maps to compare the three alternative lesion mapping methods. The age effect on lesion probability estimated from a reference data set (13,680 individuals from the UK Biobank) is used to simulate a realistic voxel-wise distribution of lesions across age. To mimic the real features of lesion masks, we propose matching brain lesion summaries (total lesion volume, average lesion size and lesion count) across the reference data set and the simulated data sets. Thus, we allow for a fair comparison between the modelling approaches, under a realistic simulation setting.
Our findings suggest that bias-reduced estimates for voxel-wise binary-response generalized linear models (GLMs) overcome the drawbacks of infinite and biased maximum likelihood estimates and scale well for large data sets because voxel-wise estimation can be performed in parallel across voxels. Contrary to the assumption of spatial dependence being key in lesion mapping, our results show that voxel-wise bias-reduction and spatial modelling result in largely similar estimates.
Bias-reduced estimates for voxel-wise GLMs are not only accurate but also computationally efficient, which will become increasingly important as more biobank-scale neuroimaging data sets become available.
脑白质病变是老年人 MRI 上的常见表现，其存在增加了中风和痴呆的风险。为了将病变发生率与高血压等危险因素联系起来，需要准确且计算效率高的建模方法。然而，在脑图谱文献中，对于二进制病变数据，是否采用基于体素的建模方法优于更具计算密集度的空间建模方法，后者考虑了体素之间的依赖性，尚未达成共识。
我们综述了用于对二进制病变掩模进行建模的三种回归方法，包括多元概率回归建模，其中使用最大似然估计或均值偏置减少估计，以及空间贝叶斯建模，其中回归系数在局部空间依赖性之前具有条件自回归模型先验。我们设计了一个新的人工病变图模拟框架，用于比较三种替代的病变映射方法。从参考数据集（来自英国生物库的 13680 个人）中估计的年龄对病变概率的影响用于模拟病变在整个年龄范围内的逼真体素分布。为了模拟病变掩模的真实特征，我们在参考数据集和模拟数据集中提出了匹配脑病变总结（总病变体积、平均病变大小和病变计数）。因此，我们可以在逼真的模拟环境下，在建模方法之间进行公平比较。
我们的研究结果表明，基于体素的二进制响应广义线性模型（GLM）的偏置减少估计克服了最大似然估计的无限性和偏倚性的缺点，并且可以很好地扩展到大数据集，因为体素估计可以在体素之间并行进行。与病变映射中空间依赖性关键的假设相反，我们的结果表明，体素偏置减少和空间建模导致的估计结果非常相似。
基于体素的 GLM 的偏置减少估计不仅准确，而且计算效率高，随着更多生物库规模的神经影像学数据集的出现，这将变得越来越重要。
