Predicting Alzheimer's disease progression using multi-modal deep learning approach
Predicting Alzheimer's disease progression using multi-modal deep learning approach
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DOI：
10.1038/s41598-018-37769-z
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发表时间：
2019-02-13
影响因子：
4.6
通讯作者：
Fargher, Kristin
中科院分区：
文献类型：
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作者：
Lee, Garam;Nho, Kwangsik;Fargher, Kristin
阿尔茨海默氏病（AD）是一种进行性神经退行性疾病，其特征是认知功能下降，没有经过验证的疾病改善治疗。在AD临床表现之前早期发现AD是及时治疗的关键。轻度认知障碍（MCI）是介于认知正常的老年人和AD之间的中间阶段。为了预测从MCI到可能的AD的转化，我们应用了一种深度学习方法，即多模态递归神经网络。我们开发了一个综合框架，不仅结合了基线时的横截面神经影像学生物标志物，还结合了从阿尔茨海默病神经影像学倡议队列（ADNI）获得的纵向脑脊液（CSF）和认知能力生物标志物。该框架集成了纵向多领域数据。我们的结果表明：1）当仅单独使用单一模态数据时，我们的MCI转化为AD的预测模型的准确率高达75%（曲线下面积（AUC）= 0.83）; 2）当合并纵向多域数据时，我们的预测模型的准确率为81%（AUC = 0.86）。多模式深度学习方法有可能识别出可能从临床试验中获益最多的AD风险人群，或作为临床试验中的分层方法。
Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.