Kumar Sayantan, Earnest Tom, Yang Braden, Kothapalli Deydeep, Aschenbrenner Andrew J, Hassenstab Jason, Xiong Chengie, Ances Beau, Morris John, Benzinger Tammie L S, Gordon Brian A, Payne Philip, Sotiras Aristeidis
Department of Computer Science and Engineering, Washington University in St Louis, Saint Louis, Missouri, USA.
Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA.
Alzheimers Dement. 2025 Apr;21(4):e70143. doi: 10.1002/alz.70143.
Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers.
We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted magnetic resonance imaging (MRI), amyloid, and tau positron emission tomography (PET). Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN.
Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression.
Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.
Normative modeling examined AD heterogeneity across multimodal imaging biomarkers. Heterogeneity in spatial patterns of gray matter atrophy, amyloid, and tau burden. Higher within-group heterogeneity for AD patients at advanced dementia stages. Patient-specific metric summarized extent of neurodegeneration and neuropathology. Metric is a marker of poor brain health and can monitor risk of disease progression.
以往的研究已将规范建模应用于单一神经影像学模态，以研究阿尔茨海默病（AD）的异质性。我们采用了基于深度学习的多模态规范框架，来分析跨ATN（淀粉样蛋白- tau -神经退行性变）成像生物标志物的个体水平差异。
我们选择了具有可用T1加权磁共振成像（MRI）、淀粉样蛋白和tau正电子发射断层扫描（PET）的横断面发现队列（n = 665）和复制队列（n = 430）。规范建模估计了淀粉样蛋白阳性个体与淀粉样蛋白阴性对照相比的个体水平异常偏差。在不同临床组水平绘制区域异常模式，以评估组内异质性。使用跨ATN异常偏差的空间范围和大小计算个体水平的疾病严重程度指数（DSI）。
在AD更严重的临床阶段，观察到ATN异常模式中更大的组内异质性。较高的DSI与较差的认知功能和疾病进展风险增加相关。
跨ATN的个体特异性异常图谱揭示了AD对大脑的异质性影响。
规范建模检查了跨多模态成像生物标志物的AD异质性。灰质萎缩、淀粉样蛋白和tau负荷的空间模式存在异质性。晚期痴呆阶段的AD患者组内异质性更高。患者特异性指标总结了神经退行性变和神经病理学的程度。该指标是脑健康不佳的标志物，可监测疾病进展风险。
