Imaging plus X: multimodal models of neurodegenerative disease.
Imaging plus X: multimodal models of neurodegenerative disease.
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DOI：
10.1097/wco.0000000000000460
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发表时间：
2017-08
影响因子：
4.8
通讯作者：
EuroPOND consortium
中科院分区：
文献类型：
--
作者：
Oxtoby NP;Alexander DC;EuroPOND consortium
本文认为，数据驱动的疾病建模在神经退行性疾病的研究和管理中占据中心地位的时间正在接近。现在临床医生可以获得的大量数据无法进行定性评估;数据类型的异质性使得通过传统统计方法进行整合变得复杂;并且可用的大型数据集仍然远远没有达到完全数据驱动的机器学习方法所需的大数据大小。最近出现的数据驱动的疾病进展模型提供了疾病特征的强加知识和从数据中学习到的模式之间的平衡。由此产生的模型既能预测个体患者的疾病进展，又能揭示潜在的生物学模式。受观察模型的启发，数据驱动的疾病进展模型在过去几年中已经成为了解神经退行性疾病发展的可行手段。这些模型揭示了对额颞叶痴呆症、亨廷顿病、多发性硬化症、帕金森病和其他疾病的认识。例如，基于事件的模型揭示了对进展模式的更精细的分级理解;自建模回归和微分方程模型提供了数据驱动的生物标志物轨迹;时空模型表明，大脑形状的变化，例如海马，可以在可检测的神经变性之前发生;网络模型为疾病传播的朊病毒样机制假说提供了一些支持。最成熟的结果是在散发性阿尔茨海默病，在很大程度上是因为阿尔茨海默病神经成像倡议数据集的可用性。结果普遍支持流行的淀粉样蛋白主导的阿尔茨海默病的假设模型，同时揭示了更精细的细节和对疾病进展的洞察。新兴的疾病进展建模领域提供了一种自然机制来整合不同类型的信息，例如来自成像、血清和脑脊液标志物以及认知测试的信息，以获得对进展性疾病的新见解。这些见解包括神经退行性变的细粒度纵向模式，从早期阶段，以及这些轨迹在人群中的异质性。更实用的是，这些模型能够更精确地进行患者分期和分层，预测进展率以及更早更好地识别风险个体。我们认为，这将使疾病进展建模对未来临床试验的招募和终点非常宝贵，可能改善试验的高失败率，例如，老年痴呆症的治疗。我们回顾了这些技术的最新进展，并讨论了将这些想法转化为一线应用所需的未来步骤。
This article argues that the time is approaching for data-driven disease modelling to take centre stage in the study and management of neurodegenerative disease. The snowstorm of data now available to the clinician defies qualitative evaluation; the heterogeneity of data types complicates integration through traditional statistical methods; and the large datasets becoming available remain far from the big-data sizes necessary for fully data-driven machine-learning approaches. The recent emergence of data-driven disease progression models provides a balance between imposed knowledge of disease features and patterns learned from data. The resulting models are both predictive of disease progression in individual patients and informative in terms of revealing underlying biological patterns. Largely inspired by observational models, data-driven disease progression models have emerged in the last few years as a feasible means for understanding the development of neurodegenerative diseases. These models have revealed insights into frontotemporal dementia, Huntington's disease, multiple sclerosis, Parkinson's disease and other conditions. For example, event-based models have revealed finer graded understanding of progression patterns; self-modelling regression and differential equation models have provided data-driven biomarker trajectories; spatiotemporal models have shown that brain shape changes, for example of the hippocampus, can occur before detectable neurodegeneration; and network models have provided some support for prion-like mechanistic hypotheses of disease propagation. The most mature results are in sporadic Alzheimer's disease, in large part because of the availability of the Alzheimer's disease neuroimaging initiative dataset. Results generally support the prevailing amyloid-led hypothetical model of Alzheimer's disease, while revealing finer detail and insight into disease progression. The emerging field of disease progression modelling provides a natural mechanism to integrate different kinds of information, for example from imaging, serum and cerebrospinal fluid markers and cognitive tests, to obtain new insights into progressive diseases. Such insights include fine-grained longitudinal patterns of neurodegeneration, from early stages, and the heterogeneity of these trajectories over the population. More pragmatically, such models enable finer precision in patient staging and stratification, prediction of progression rates and earlier and better identification of at-risk individuals. We argue that this will make disease progression modelling invaluable for recruitment and end-points in future clinical trials, potentially ameliorating the high failure rate in trials of, e.g., Alzheimer's disease therapies. We review the state of the art in these techniques and discuss the future steps required to translate the ideas to front-line application.