Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA; The Florey Institute, The University of Melbourne, Victoria, Australia; Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia.
Neuroimage. 2019 Feb 1;186:446-454. doi: 10.1016/j.neuroimage.2018.11.019. Epub 2018 Nov 17.
There is a growing need in clinical research domains for direct comparability between amyloid-beta (Aβ) Positron Emission Tomography (PET) measures obtained via different radiotracers and processing methodologies. Previous efforts to provide a common measurement scale fail to account for non-linearities between measurement scales that can arise from these differences. We introduce a new application of distribution mapping, based on well established statistical orthodoxy, that we call Nonlinear Distribution Mapping (NoDiM). NoDiM uses cumulative distribution functions to derive mappings between Aβ-PET measurements from different tracers and processing streams that align data based on their location in their respective distributions.
Utilizing large datasets of Florbetapir (FBP) from the Alzheimer's Disease Neuroimaging Initiative (n = 349 female (%) = 53) and Pittsburgh Compound B (PiB) from the Harvard Aging Brain Study (n = 305 female (%) = 59.3) and the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (n = 184 female (%) = 53.3), we fit explicit mathematical models of a mixture of two normal distributions, with parameter estimates from Gaussian Mixture Models, to each tracer's empirical data. We demonstrate the accuracy of these fits, and then show the ability of NoDiM to transform FBP measurements into PiB-like units.
A mixture of two normal distributions fit both the FBP and PiB empirical data and provides a strong basis for derivation of a transfer function. Transforming Aβ-PET data with NoDiM results in FBP and PiB distributions that are closely aligned throughout their entire range, while a linear transformation does not. Additionally the NoDiM transform better matches true positive and false positive profiles across tracers.
The NoDiM transformation provides a useful alternative to the linear mapping advocated in the Centiloid project, and provides improved correspondence between measurements from different tracers across the range of observed values. This improved alignment enables disparate measures to be merged on to continuous scale, and better enables the use of uniform thresholds across tracers.
在临床研究领域，人们越来越需要对通过不同示踪剂和处理方法获得的淀粉样蛋白-β（Aβ）正电子发射断层扫描（PET）测量值进行直接比较。以前，为提供通用测量尺度所做的努力未能考虑到可能由于这些差异而产生的测量尺度之间的非线性关系。我们引入了一种新的分布映射应用，该应用基于成熟的统计正统理论，我们称之为非线性分布映射（NoDiM）。NoDiM 使用累积分布函数来推导来自不同示踪剂和处理流的 Aβ-PET 测量值之间的映射，该映射根据其在各自分布中的位置对数据进行对齐。
利用来自阿尔茨海默病神经影像学倡议（n=349 名女性（%=53）的 Florbetapir（FBP）和哈佛衰老大脑研究（n=305 名女性（%=59.3）的 Pittsburgh 复合 B（PiB）以及澳大利亚成像、生物标志物和生活方式老化旗舰研究（n=184 名女性（%=53.3）的大型数据集，我们对每个示踪剂的经验数据拟合了两个正态分布混合的显式数学模型，参数估计来自高斯混合模型。我们展示了这些拟合的准确性，然后展示了 NoDiM 将 FBP 测量值转换为 PiB 样单位的能力。
两个正态分布的混合很好地拟合了 FBP 和 PiB 的经验数据，为推导传递函数提供了坚实的基础。使用 NoDiM 转换 Aβ-PET 数据会导致 FBP 和 PiB 分布在整个范围内紧密对齐，而线性转换则不会。此外，NoDiM 转换更好地匹配了示踪剂之间的真阳性和假阳性分布。
NoDiM 转换为 Centiloid 项目中提倡的线性映射提供了一种有用的替代方法，并在观察值范围内改善了来自不同示踪剂的测量值之间的对应关系。这种改进的对齐方式使不同的测量值能够合并到连续的尺度上，并能够更好地在示踪剂之间使用统一的阈值。
