Lawry Aguila Ana, Lorenzini Luigi, Janahi Mohammed, Barkhof Frederik, Altmann Andre
Department of Medical Physics and Biomedical Engineering, UCL Hawkes Institute, University College London (UCL), London, UK.
Present Address: Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School and Massachusetts General Hospital, Boston, USA.
Alzheimers Res Ther. 2025 May 15;17(1):107. doi: 10.1186/s13195-025-01753-3.
Exploring the early stages of Alzheimer's disease (AD) is crucial for timely intervention to help manage symptoms and set expectations for affected individuals and their families. However, the study of the early stages of AD involves analysing heterogeneous disease cohorts which may present challenges for some modelling techniques. This heterogeneity stems from the diverse nature of AD itself, as well as the inclusion of undiagnosed or 'at-risk' AD individuals or the presence of comorbidities which differentially affect AD biomarkers within the cohort. Normative modelling is an emerging technique for studying heterogeneous disorders that can quantify how brain imaging-based measures of individuals deviate from a healthy population. The normative model provides a statistical description of the 'normal' range that can be used at subject level to detect deviations, which may relate to pathological effects.
In this work, we applied a deep learning-based normative model, pre-trained on MRI scans in the UK Biobank, to investigate ageing and identify abnormal age-related decline. We calculated deviations, relative to the healthy population, in multi-modal MRI data of non-demented individuals in the external EPAD (ep-ad.org) cohort and explored these deviations with the aim of determining whether normative modelling could detect AD-relevant subtle differences between individuals.
We found that aggregate measures of deviation based on the entire brain correlated with measures of cognitive ability and biological phenotypes, indicating the effectiveness of a general deviation metric in identifying AD-related differences among individuals. We then explored deviations in individual imaging features, stratified by cognitive performance and genetic risk, across different brain regions and found that the brain regions showing deviations corresponded to those affected by AD such as the hippocampus. Finally, we found that 'at-risk' individuals in the EPAD cohort exhibited increasing deviation over time, with an approximately 6.4 times greater t-statistic in a pairwise t-test compared to a 'super-healthy' cohort.
This study highlights the capability of deep normative modelling approaches to detect subtle differences in brain morphology among individuals at risk of developing AD in a non-demented population. Our findings allude to the potential utility of normative deviation metrics in monitoring disease progression.
探索阿尔茨海默病（AD）的早期阶段对于及时干预以帮助控制症状以及为患者及其家庭设定预期至关重要。然而，AD早期阶段的研究涉及分析异质性疾病队列，这可能给一些建模技术带来挑战。这种异质性源于AD本身的多样性，以及纳入了未确诊或“有风险”的AD个体，或存在共病，这些因素会对队列中的AD生物标志物产生不同影响。规范建模是一种用于研究异质性疾病的新兴技术，它可以量化基于脑成像的个体测量值与健康人群的差异程度。规范模型提供了一个“正常”范围的统计描述，可用于个体水平检测可能与病理效应相关的偏差。
在这项研究中，我们应用了一种基于深度学习的规范模型，该模型在英国生物银行的MRI扫描数据上进行了预训练，以研究衰老并识别与年龄相关的异常衰退。我们计算了外部EPAD（ep - ad.org）队列中未患痴呆个体的多模态MRI数据相对于健康人群的偏差，并探索这些偏差，目的是确定规范建模是否能够检测个体之间与AD相关的细微差异。
我们发现基于全脑的综合偏差测量值与认知能力和生物学表型测量值相关，这表明通用偏差度量在识别个体之间与AD相关差异方面的有效性。然后，我们按认知表现和遗传风险分层，探索了不同脑区个体成像特征的偏差，发现显示偏差的脑区与受AD影响的脑区（如海马体）相对应。最后，我们发现EPAD队列中的“有风险”个体随时间偏差增加，在成对t检验中，其t统计量比“超级健康”队列大约高６.４倍。
本研究强调了深度规范建模方法在检测非痴呆人群中具有AD发病风险个体之间脑形态细微差异方面的能力。我们的研究结果暗示了规范偏差度量在监测疾病进展方面的潜在效用。
