Data_Sheet_1_Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer’s disease continuum.pdf

IntroductionTau PET imaging has emerged as an important tool to detect and monitor tangle burden in vivo in the study of Alzheimer’s disease (AD). Previous studies demonstrated the association of tau burden with cognitive decline in probable AD cohorts. This study introduces a novel approach to analyze tau PET data by constructing individualized tau network structure and deriving its graph theory-based measures. We hypothesize that the network- based measures are a measure of the total tau load and the stage through disease.
MethodsUsing tau PET data from the AD Neuroimaging Initiative from 369 participants, we determine the network measures, global efficiency, global strength, and limbic strength, and compare with two regional measures entorhinal and tau composite SUVR, in the ability to differentiate, cognitively unimpaired (CU), MCI and AD. We also investigate the correlation of these network and regional measures and a measure of memory performance, auditory verbal learning test for long-term recall memory (AVLT-LTM). Finally, we determine the stages based on global efficiency and limbic strength using conditional inference trees and compare with Braak staging.
ResultsWe demonstrate that the derived network measures are able to differentiate three clinical stages of AD, CU, MCI, and AD. We also demonstrate that these network measures are strongly correlated with memory performance overall. Unlike regional tau measurements, the tau network measures were significantly associated with AVLT-LTM even in cognitively unimpaired individuals. Stages determined from global efficiency and limbic strength, visually resembled Braak staging.
DiscussionThe strong correlations with memory particularly in CU suggest the proposed technique may be used to characterize subtle early tau accumulation. Further investigation is ongoing to examine this technique in a longitudinal setting.

引言：Tau PET成像（Tau PET imaging）已成为阿尔茨海默病（Alzheimer’s Disease, AD）研究中体内检测与监测神经原纤维缠结负荷的重要工具。既往研究表明，在拟诊AD队列中，tau负荷与认知衰退存在关联。本研究提出一种全新的tau PET数据分析方法：通过构建个体化tau神经网络结构，并提取基于图论（graph theory）的相关指标，本研究假设该神经网络指标可反映整体tau负荷及疾病进展阶段。

研究方法：本研究使用来自阿尔茨海默病神经影像倡议（AD Neuroimaging Initiative, ADNI）的369名受试者的tau PET数据，计算全局效率（global efficiency）、全局强度（global strength）、边缘系统强度（limbic strength）等神经网络指标，并与内嗅皮层（entorhinal）tau复合标准化摄取值比值（SUVR）这两项区域指标对比，以评估其区分认知正常（cognitively unimpaired, CU）、轻度认知障碍（MCI）与AD的能力。此外，本研究还分析了上述神经网络指标、区域指标与记忆表现指标——听觉语言学习测试长期回忆记忆（auditory verbal learning test for long-term recall memory, AVLT-LTM）之间的相关性。最后，本研究采用条件推理树（conditional inference trees）基于全局效率与边缘系统强度划分疾病阶段，并与Braak分期（Braak staging）进行对比。

研究结果：本研究证实，提取得到的神经网络指标可有效区分AD的三大临床阶段——认知正常、轻度认知障碍与AD。同时，本研究发现上述神经网络指标整体上与记忆表现显著相关。与区域tau测量指标不同的是，即使在认知正常受试者中，tau神经网络指标仍与AVLT-LTM存在显著关联。基于全局效率与边缘系统强度划分的疾病阶段，在视觉上与Braak分期具有相似性。

讨论：本研究提出的方法与记忆表现存在强相关性，尤其在认知正常受试者中，这提示该技术可用于表征早期细微的tau蛋白聚集。目前本团队正在开展后续研究，以在纵向队列中验证该方法的有效性。