Wellcome Trust Center for Neuroimaging, Institute of Neurology, London, UK; Department of Psychiatry, Jena University Hospital, Jena, Germany.
Wellcome Trust Center for Neuroimaging, Institute of Neurology, London, UK.
Neuroimage. 2014 Aug 15;97:333-48. doi: 10.1016/j.neuroimage.2014.04.018. Epub 2014 Apr 15.
Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease.
基于 MRI 的结构成像已成为潜在痴呆症患者临床评估的一个组成部分。我们在这里提出了一种基于个体高斯过程的推理方案，用于对健康和病理性衰老老年受试者进行临床决策支持。该方法旨在为临床医生提供定量和透明的支持，帮助他们检测阿尔茨海默病或其他类型痴呆症患者的结构异常。首先，我们引入了一个生成模型，其中包含了我们关于大脑中局部和整体灰质体积随年龄正常下降的知识。通过假设结构轨迹平滑，该模型解释了与年龄相关的结构下降的一般过程以及老年加速损失。考虑到健康受试者的人口统计学和大脑整体参数可以提供关于正常大脑老化变异性的信息，从而为单个病例提供个性化预测。使用高斯过程模型作为参考，可以预测新受试者的大脑扫描，并通过正态概率图（NPM）和全局 z 分数来量化局部灰质异常。通过整合观察到的期望误差和预测不确定性，局部图谱和全局评分利用了贝叶斯推断在临床决策中的优势，并为病理性老化的诊断信息提供了有价值的扩展。我们在模拟数据和真实 MRI 数据中验证了该方法。我们使用年龄在 18-94 岁之间的 1238 名健康受试者训练了 GP 框架，并对被诊断为健康对照组、轻度认知障碍和阿尔茨海默病的 415 名独立测试受试者进行了预测。
