Weiner Michael W, Veitch Dallas P, Aisen Paul S, Beckett Laurel A, Cairns Nigel J, Green Robert C, Harvey Danielle, Jack Clifford R, Jagust William, Morris John C, Petersen Ronald C, Saykin Andrew J, Shaw Leslie M, Toga Arthur W, Trojanowski John Q
Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA.
Alzheimers Dement. 2017 Apr;13(4):e1-e85. doi: 10.1016/j.jalz.2016.11.007. Epub 2017 Mar 22.
The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.
We used standard searches to find publications using ADNI data.
(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.
Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
阿尔茨海默病神经影像学计划（ADNI）持续推进生物标志物方法的开发与标准化，并为合格研究人员提供了深度和广度均有所增加的数据。本综述总结了2014年至2015年期间使用ADNI数据的400多篇出版物。
我们采用标准检索方法查找使用ADNI数据的出版物。
（1）在海马萎缩之前的症状前受试者中，可检测到结构和功能变化，包括海马形状和纹理的细微变化、海马以外区域的萎缩以及功能网络的破坏；（2）在β-淀粉样蛋白沉积异常（Aβ+）的受试者中，生物标志物按照淀粉样蛋白级联假说预测的顺序变得异常；（3）认知衰退与tau的关联比与Aβ沉积更为密切；（4）脑血管危险因素可能与Aβ相互作用，增加白质（WM）异常，这可能与tau异常一起加速阿尔茨海默病（AD）的进展；（5）不同的萎缩模式与记忆和执行功能受损相关，可能是精神症状的基础；（6）随着AD的进展，结构、功能和代谢网络连接性受到破坏。Aβ病理沿WM束的朊病毒样传播模型预测了皮质Aβ沉积的已知模式以及葡萄糖代谢的下降；（7）使用生物学信息方法已鉴定出新的AD风险和保护基因位点；（8）认知正常和轻度认知障碍（MCI）受试者具有异质性，包括不仅以“经典”AD病理为特征的群体，还包括生物标志物正常、衰退加速和疑似非阿尔茨海默病病理的群体；（9）根据一种或多种病理选择即将衰退风险的受试者可提高临床试验的效能；（10）认知结果测量对认知早期变化的敏感性已得到提高，使用纵向结构磁共振成像的替代结果测量可能进一步降低临床试验成本和持续时间；（11）神经网络等机器学习技术的进展提高了诊断和预后准确性，尤其是在涉及MCI受试者的挑战中；（12）网络连接性测量和基因变异在多模态分类中显示出前景，一些使用单一模态的分类器正在与多模态分类器竞争。
总体而言，这些研究从根本上加深了我们对AD进展及其潜在遗传基础的理解，这反过来又为临床试验设计提供了信息并加以改进。
