A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework
A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework
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DOI：
10.1109/jbhi.2018.2863202
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发表时间：
2019-01-01
影响因子：
7.7
通讯作者：
Bezerianos, Anastasios
中科院分区：
文献类型：
--
作者：
Dragomir, Andrei;Vrahatis, Aristidis G.;Bezerianos, Anastasios
由于预期寿命的延长，预计未来几十年阿尔茨海默病 (AD) 的患病率和影响将大幅上升，从而导致医疗费用大幅增加。虽然诊断时的大脑功能障碍是不可逆转的，但人们普遍认为 AD 病理学在临床症状出现前几十年就已形成。如果能够在疾病进展的早期检测到初期过程，就可以设计预防或减缓疾病的前瞻性干预措施。目前，没有可用于检测和监测疾病进展早期阶段的非侵入性生物标志物。 AD 的复杂病因学需要基于系统的方法来支持多模式和多级数据的集成，而基于网络的建模为揭示由疾病引发的复杂系统级破坏的方法提供了支架。在这项工作中，我们回顾了当前的最新技术，重点关注分子和大脑功能连接水平上基于网络的生物标志物。特别强调概述最近的趋势，强调分子和连接网络中模块化子结构的功能重要性及其潜在的生物标志物价值。我们的观点植根于网络医学，总结了识别基于网络的生物标志物的流程，以及整合基因型和大脑表型信息以在 AD 早期诊断中采用全面无创方法的好处。最后，我们提出了一个整合分子和大脑连接水平知识的框架，该框架有潜力实现无创诊断，为监测治疗提供支持，并帮助理解迄今为止未经检验的基因型和大脑表型之间的深层关系。
A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.