[论文解读] Loss of inter-frequency brain hubs in Alzheimer's disease

Jérémy Guillon, Yohan Attal+6 more|arXiv (Cornell University)|Dec 31, 2016

Functional Brain Connectivity Studies被引用 4

一句话总结

本研究提出一种多层网络框架，利用磁脑图（MEG）分析阿尔茨海默病（AD）患者与对照组的多频段脑网络。研究发现，AD会破坏跨频段枢纽连接，尤其在扣带皮层和默认模式网络中，导致多参与系数（MPC）降低，该现象与记忆衰退相关，并将诊断准确率提升至78.39%。

ABSTRACT

Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-beta deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.

研究动机与目标

• 研究阿尔茨海默病如何改变多个频段上的功能性脑网络组织结构。
• 识别可能导致AD认知衰退的跨频段连接枢纽的破坏。
• 评估多频段网络度量在区分AD与健康老龄化方面的诊断潜力。
• 检验网络枢纽 dysfunc­tion 与AD患者临床记忆损伤之间的关系。

提出的方法

• 从多个频段（如delta、theta、alpha、beta、gamma）的静息态磁脑图（MEG）记录中构建多层脑网络。
• 应用多参与系数（MPC）量化脑区在不同频段中作为枢纽的程度。
• 比较AD患者与年龄匹配对照组的MPC分布及网络拓扑结构。
• 将MPC值与临床记忆评分（总 recall 分数）相关联，以评估其功能相关性。
• 将MPC用作机器学习分类器中的特征，评估AD的诊断性能。

实验结果

研究问题

• RQ1 阿尔茨海默病如何改变脑区在振荡频段之间枢纽分布的模式？
• RQ2 在健康老龄化中，哪些脑区作为关键的跨频段枢纽发挥作用，且在AD中如何被破坏？
• RQ3 跨频段枢纽功能丧失在多大程度上与AD患者的记忆损伤相关？
• RQ4 多参与系数（MPC）能否提升阿尔茨海默病的诊断分类准确率？

主要发现

• AD患者表现出显著降低的多参与系数（MPC），表明其跨频段枢纽连接功能受损，相较于对照组。
• 扣带皮层和联合区是AD中跨频段枢纽功能受损的主要脑区。
• MPC值与记忆损伤显著相关，以AD受试者的总回忆分数衡量。
• 默认模式网络成分，尤其是易受萎缩和淀粉样蛋白β沉积影响的区域，对MPC变化最具预测性。
• 将MPC用作诊断特征时，在区分AD与对照组中实现了78.39%的分类准确率和91.11%的敏感度。
• 多层网络方法揭示，跨频段连接是AD中被破坏的关键功能机制，具有可测量的临床与诊断相关性。