Prediction of Autopsy Verified Neuropathological Change of Alzheimer's Disease Using Machine Learning and MRI
Prediction of Autopsy Verified Neuropathological Change of Alzheimer's Disease Using Machine Learning and MRI
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DOI：
10.3389/fnagi.2018.00406
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发表时间：
2018-12-10
影响因子：
4.8
通讯作者：
Lanzenberger, Rupert
中科院分区：
文献类型：
--
作者：
Kautzky, Alexander;Seiger, Rene;Lanzenberger, Rupert
背景：阿尔茨海默病（Alzheimer's disease，AD）是痴呆症中最常见的一种。虽然AD的神经病理学改变已被确定，但在临床环境中认知障碍之前的AD的早期检测仍然缺乏。将机器学习应用于磁共振成像（MRI）数据以预测轻度认知障碍（MCI）或AD的先驱研究已经取得了很高的准确性，然而，仍然缺乏预测神经病理学变化的算法。本研究的目的是计算一个预测模型，该模型支持与临床表现相比更明显的AD诊断标准，甚至可以在症状出现之前识别标志性变化。尸检证实了归因于AD的神经病理学变化，如由国家老龄化研究所-阿尔茨海默氏症协会（NIAA）发布的4肽、神经纤维缠结和神经炎斑块的组合评分所描述的，AD ABC评分是用RandomForest（RF）从结构MRI数据预测的。在死亡前至少2年进行MRI扫描。所有受试者均来自前瞻性维也纳跨多瑙河老龄化（VITA）研究，该研究针对维也纳两个区2000年开始的所有1 750名75岁以上的居民，包括不定期随访直至死亡，无论临床症状或诊断如何。68例受试者的MRI和神经病理学数据可用，49例受试者（死亡时平均年龄：82.8 ± 2.9岁，29例女性）的MRI数据质量足够高，入组使用嵌套交叉验证（CV）进行进一步统计分析。内环的解码数据用于五倍CV设计的变量选择和参数优化，外环的新数据用于五倍CV设计的最佳设置的模型验证。整个过程进行了10次，平均准确度与标准偏差reported.Results：最翔实的ROLS包括尾侧和喙侧前扣带回，内嗅，梭状和岛皮质和皮质下ROLS胼胝体和左血管，ROI包括下壳核和苍白球的腔隙性改变。所得到的预测模型实现了平均准确度为三个层次的NIAA AD得分为0.62内的解码集和0.61的验证集。更高的准确率为0.77，分别为两组，预测存在或不存在的神经病理学变化时，达到结论：计算机辅助预测的神经病理学变化根据分类NIAA评分在AD，目前只能评估死后，可能有利于更明显和明确的分类AD痴呆。AD的神经病理学特征的可靠检测将能够在比MCI或临床AD症状的预测更早的水平上进行风险分层，并在神经精神病学中推进精确医学。
Background: Alzheimer's disease (AD) is the most common form of dementia. While neuropathological changes pathognomonic for AD have been defined, early detection of AD prior to cognitive impairment in the clinical setting is still lacking. Pioneer studies applying machine learning to magnetic-resonance imaging (MRI) data to predict mild cognitive impairment (MCI) or AD have yielded high accuracies, however, an algorithm predicting neuropathological change is still lacking. The objective of this study was to compute a prediction model supporting a more distinct diagnostic criterium for AD compared to clinical presentation, allowing identification of hallmark changes even before symptoms occur.Methods: Autopsy verified neuropathological changes attributed to AD, as described by a combined score for 4-peptides, neurofibrillary tangles and neuritic plaques issued by the National Institute on Aging - Alzheimer's Association (NIAA), the ABC score for AD, were predicted from structural MRI data with RandomForest (RF). MRI scans were performed at least 2 years prior to death. All subjects derive from the prospective Vienna Trans-Danube Aging (VITA) study that targeted all 1750 inhabitants of the age of 75 in the starting year of 2000 in two districts of Vienna and included irregular follow-ups until death, irrespective of clinical symptoms or diagnoses. For 68 subjects MRI as well as neuropathological data were available and 49 subjects (mean age at death: 82.8 +/- 2.9, 29 female) with sufficient MRI data quality were enrolled for further statistical analysis using nested cross-validation (CV). The decoding data of the inner loop was used for variable selection and parameter optimization with a fivefold CV design, the new data of the outer loop was used for model validation with optimal settings in a fivefold CV design. The whole procedure was performed ten times and average accuracies with standard deviations were reported.Results: The most informative ROls included caudal and rostral anterior cingulate gyrus, entorhinal, fusiform and insular cortex and the subcortical ROls anterior corpus callosum and the left vessel, a ROI comprising lacunar alterations in inferior putamen and pallidum. The resulting prediction models achieved an average accuracy for a three leveled NIAA AD score of 0.62 within the decoding sets and of 0.61 for validation sets. Higher accuracies of 0.77 for both sets, respectively, were achieved when predicting presence or absence of neuropathological change.Conclusion: Computer-aided prediction of neuropathological change according to the categorical NIAA score in AD, that currently can only be assessed post-mortem, may facilitate a more distinct and definite categorization of AD dementia. Reliable detection of neuropathological hallmarks of AD would enable risk stratification at an earlier level than prediction of MCI or clinical AD symptoms and advance precision medicine in neuropsychiatry.