用灵活和分层多变量测量误差模型综合纵向皮质厚度估计
Synthesizing longitudinal cortical thickness estimates with a flexible and hierarchical multivariate measurement-error model
摘要：
基于MRI的内嗅皮质厚度（eCT）测量可预测阿尔茨海默病（AD）的认知功能下降，成本低，侵入性小。FreeSurfer（FS）和高级标准化工具（ANT）这两种突出的成像范例具有用于从原始MRI中提取区域特定eCT测量值的多个管道，但这些管道的绝对复杂性使得难以在管道之间进行选择，比较管道之间的结果，并表征管道估计的不确定性。更糟糕的是，EC特别难以成像，导致管线之间的厚度估计的变化，其压倒了AD的生理变化预测。我们检查了阿尔茨海默病神经影像倡议的七个不同的MRI管道的eCT输出。由于理论和实践的局限性，我们没有金标准来评估它们。相反，我们使用贝叶斯分层模型来组合估计值。所得到的后验分布产生高概率理想化eCT值，其通过支持不同恒定偏移、标准差、拖尾和管道之间的相关性结构的灵活的多变量误差模型来解释固有的不确定性。我们的分层模型直接将理想化的eCT与临床结局联系起来，将eCT估计的不确定性传播到临床估计，同时考虑数据中的纵向结构。令人惊讶的是，尽管它在先验提供的预测和正则化中包含了更大的不确定性，但组合模型揭示了eCT与认知能力之间的关联性，而不是仅基于单个管道数据的非分层模型。
英文摘要：
MRI-based entorhinal cortical thickness (eCT) measurements predict cognitive decline in Alzheimer's disease (AD) with low cost and minimal invasiveness. Two prominent imaging paradigms, FreeSurfer (FS) and Advanced Normalization Tools (ANTs), feature multiple pipelines for extracting region-specific eCT measurements from raw MRI, but the sheer complexity of these pipelines makes it difficult to choose between pipelines, compare results between pipelines, and characterize uncertainty in pipeline estimates. Worse yet, the EC is particularly difficult to image, leading to variations in thickness estimates between pipelines that overwhelm physiologicl variations predictive of AD. We examine the eCT outputs of seven different pipelines on MRIs from the Alzheimer's Disease Neuroimaging Initiative. Because of both theoretical and practical limitations, we have no gold standard by which to evaluate them. Instead, we use a Bayesian hierarchical model to combine the estimates. The resulting posterior distribution yields high-probability idealized eCT values that account for inherent uncertainty through a flexible multivariate error model that supports different constant offsets, standard deviations, tailedness, and correlation structures between pipelines. Our hierarchical model directly relates idealized eCTs to clinical outcomes in a way that propagates eCT estimation uncertainty to clinical estimates while accounting for longitudinal structure in the data. Surprisingly, even though it incorporates greater uncertainty in the predictor and regularization provided by the prior, the combined model reveals a stronger association between eCT and cognitive capacity than do nonhierarchical models based on data from single pipelines alone.
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