Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD. We develop an integrated biomarker generation- using partial least squares regression- and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable vs. progressive MCI), determines individual profiles from baseline (i.e. cognitive or biological) data and predicts individual cognitive trajectories (i.e. change in memory scores from baseline). We demonstrate that a metric learning model trained on baseline cognitive data (memory, executive function, affective measurements) discriminates stable vs. progressive MCI individuals with high accuracy (81.4%), revealing an interaction between cognitive (memory, executive functions) and affective scores that may relate to MCI comorbidity (e.g. affective disturbance). Training the model to perform the same binary classification on biological data (mean cortical β-amyloid burden, grey matter density, APOE 4) results in similar prediction accuracy (81.9%). Extending beyond binary classifications, we develop and implement a trajectory modelling approach that shows significantly better performance in predicting individualised rate of future cognitive decline (i.e. change in memory scores from baseline), when the metric learning model is trained with biological (r = -0.68) compared to cognitive (r = -0.4) data. Our trajectory modelling approach reveals interpretable and interoperable markers of progression to AD and has strong potential to guide effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification, that is critical for clinical practice and discovery of personalised interventions.

中文翻译：

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对阿尔茨海默病引起的认知能力下降的预后轨迹进行建模。

阿尔茨海默病（AD）的特点是神经认知变化的动态过程，从正常认知到轻度认知障碍（MCI）并进展为痴呆。然而，并非所有 MCI 患者都会发展为痴呆症。预测患有 MCI 的个体是否会下降（即进行性 MCI）或保持稳定（即稳定 MCI）会受到患者异质性的阻碍，因为合并症可能导致 MCI 诊断而不进展为 AD。尽管早期诊断 AD 对于预后和个性化干预非常重要，但我们仍然缺乏预测个体痴呆进展的强大工具。在这里，我们提出了一种基于度量学习（广义度量学习矢量量化）的新型轨迹建模方法，该方法挖掘来自阿尔茨海默病神经影像计划（ADNI）队列中 MCI 患者的多模态数据，以得出 AD 所致认知衰退的个体化预后评分。我们开发了一种综合生物标志物生成方法（使用偏最小二乘回归）和分类方法，该方法超越了二元患者分类，分为离散亚组（即稳定与进展性MCI），从基线（即认知或生物）数据确定个体概况并预测个体认知轨迹（即记忆分数相对于基线的变化）。我们证明，基于基线认知数据（记忆、执行功能、情感测量）训练的度量学习模型能够以高精度（81.4％）区分稳定与进展的 MCI 个体，揭示认知（记忆、执行功能）和情感分数之间的相互作用可能与 MCI 合并症（例如情感障碍）有关。训练模型对生物数据（平均皮质 β-淀粉样蛋白负荷、灰质密度、APOE 4）执行相同的二元分类会产生相似的预测精度 (81.9%)。除了二元分类之外，我们开发并实施了一种轨迹建模方法，当使用生物（r = -0.68 ）与认知（r = -0.4）数据相比。我们的轨迹建模方法揭示了 AD 进展的可解释和可互操作的标记，并且具有强大的潜力来指导基于预后疾病轨迹的有效个体分层，减少 MCI 患者的错误分类，这对于临床实践和发现个性化干预措施至关重要。