Gaussian process uncertainty in age estimation as a measure of brain abnormality
Gaussian process uncertainty in age estimation as a measure of brain abnormality
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DOI：
10.1016/j.neuroimage.2018.03.075
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发表时间：
2018-07-15
期刊：
影响因子：
5.7
通讯作者：
Wachinger, Christian
中科院分区：
文献类型：
--
作者：
Becker, Benjamin Gutierrez;Klein, Tassilo;Wachinger, Christian
用于年龄估计的多元回归模型是评估与神经病理学相关的异常大脑形态的有力工具。年龄预测模型建立在健康受试者群体的基础上，旨在反映正常的衰老模式。在神经病理学呈现与加速衰老相似的退行性模式的假设下，将这些多变量模型应用于患病受试者通常会导致较高的预测误差。在这项工作中，我们提出了另一种观点，即病理学遵循与正常衰老相似的轨迹。相反，我们建议使用衡量平均老化轨迹偏差的指标。我们建议使用两种不同的指标来衡量这些偏差：高斯过程回归模型中的不确定性和新提出的年龄加权不确定性度量。因此，我们的方法假设病理性大脑模式与正常衰老的大脑模式不同。我们展示了自闭症、轻度认知障碍和阿尔茨海默病受试者的结果，以强调该方法针对不同疾病和年龄范围的多功能性。我们评估体积、厚度和 VBM 特征以量化大脑形态。我们的评估是对从各种公开的神经影像数据库获得的大量图像进行的。在所有特征中，我们基于不确定性的测量结果比预测误差更好地区分了患病受试者和健康个体。最后，我们说明了疾病模式与正常衰老的差异，支持应用不确定性作为神经病理学的衡量标准。
Multivariate regression models for age estimation are a powerful tool for assessing abnormal brain morphology associated to neuropathology. Age prediction models are built on cohorts of healthy subjects and are built to reflect normal aging patterns. The application of these multivariate models to diseased subjects usually results in high prediction errors, under the hypothesis that neuropathology presents a similar degenerative pattern as that of accelerated aging. In this work, we propose an alternative to the idea that pathology follows a similar trajectory than normal aging. Instead, we propose the use of metrics which measure deviations from the mean aging trajectory. We propose to measure these deviations using two different metrics: uncertainty in a Gaussian process regression model and a newly proposed age weighted uncertainty measure. Consequently, our approach assumes that pathologic brain patterns are different to those of normal aging. We present results for subjects with autism, mild cognitive impairment and Alzheimer's disease to highlight the versatility of the approach to different diseases and age ranges. We evaluate volume, thickness, and VBM features for quantifying brain morphology. Our evaluations are performed on a large number of images obtained from a variety of publicly available neuroimaging databases. Across all features, our uncertainty based measurements yield a better separation between diseased subjects and healthy individuals than the prediction error. Finally, we illustrate differences in the disease pattern to normal aging, supporting the application of uncertainty as a measure of neuropathology.