ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia.
ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia.
Neuroimage. 2023 Sep;278:120279. doi: 10.1016/j.neuroimage.2023.120279. Epub 2023 Jul 15.
The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual's progression through Alzheimer's disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend in expected biomarker evolution in affected individuals.
最近对阿尔茨海默病（AD）的生物学重新定义促使开发了统计模型，这些模型将生物标志物的变化与神经退行性变和与 AD 相关的病情恶化联系起来。衡量这些变化的能力可能有助于对受影响个体进行更早的诊断，并有助于监测其病情的演变。在这些统计工具中，疾病进展模型（DPM）是定量的、数据驱动的方法，专门尝试描述与 AD 相关的生物标志物的时间动态。由于这种疾病具有异质性，相同年龄的患者经历不同的 AD 相关变化，因此基于纵向混合效应的 DPM 面临的挑战是估计患者重新对齐的时间偏移。这些时间偏移对于有意义的生物标志物建模是必不可少的，但在联合估计的模型中可能会影响拟合时间或随缺失数据而变化。在这项工作中，我们通过使用主成分分析的概率公式将多个生物标志物组合成一个单一值，来估计个体在阿尔茨海默病中的进展。我们的结果表明，当我们对来自阿尔茨海默病神经影像学倡议（ADNI）的横截面数据进行基线访问时，这个通过可观察生物标志物总结 AD 的变量与联合估计的时间偏移非常相似。复制临床数据集的预期特性，我们确认估计得分对缺失数据或不可用的生物标志物具有稳健性。除了横截面见解外，我们还可以通过在后续检查中重复估计并随着更多数据的收集来细化长期估计，将潜在变量建模为个体进展得分，这在临床环境中是理想的。最后，我们验证了我们的得分可以用作伪时间尺度而不是年龄，以忽略队列数据中的一些患者异质性，并突出受影响个体中预期生物标志物演变的总体趋势。
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