Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments over Progressions
Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments over Progressions
复制标题
通过全局对齐的成像生物标志物丰富进展来改进认知结果的预测
DOI：
10.1007/978-3-030-32251-9_16
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Shen, L
中科院分区：
文献类型：
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作者：
Elbeleidy, S;Baker, L;Wang, H;Huang, H;Shen, L
目的近年来，纵向神经影像数据被广泛用于预测阿尔茨海默病(AD)自动诊断的临床评分。然而，患者不完整的颞叶神经成像记录对使用这些数据准确诊断AD构成了重大挑战。在本文中，我们提出了一种新的方法来学习丰富的成像生物标志物表示，该方法同时捕捉了研究队列中所有参与者的基线神经成像记录所传达的信息以及每个参与者可用随访记录的渐进变化。方法考虑到不同参与者通常在不同的时间点获取不同数量的医疗记录，我们提出了一个健壮的学习目标，该目标使非平方范数距离的总和最小化，尽管这在一般情况下是很难有效地解决的。结果我们使用阿尔茨海默病神经成像计划(ADNI)数据集进行了广泛的实验。当我们使用新方法学习到的丰富的生物标记物表示来预测不同的认知分数时，已经获得了明显的性能收益。我们进一步观察到，我们提出的方法所选择的顶级生物标记物与现有临床AD研究中的已知知识完全一致。结论所有这些有前景的实验结果都证明了我们的新方法的有效性。有意义我们预计我们的新方法不仅对AD研究感兴趣，而且已经在线开放了我们的方法的代码。
ObjectiveLongitudinal neuroimaging data have been widely used to predict clinical scores for automatic diagnosis of Alzheimer’s Disease (AD) in recent years. However, incomplete temporal neuroimaging records of the patients pose a major challenge to use these data for accurately diagnosing AD. In this paper, we propose a novel method to learn an enriched representation for imaging biomarkers, which simultaneously captures the information conveyed by both the baseline neuroimaging records of all the participants in a studied cohort and the progressive variations of the available follow-up records of every individual participant.MethodsTaking into account that different participants usually take different numbers of medical records at different time points, we develop a robust learning objective that minimizes the summations of a number of not-squared-norm distances, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative algorithm with rigorously proved convergence.ResultsWe have conducted extensive experiments using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Clear performance gains have been achieved when we predict different cognitive scores using the enriched biomarker representations learned by our new method. We further observe that the top selected biomarkers by our proposed method are in perfect accordance with the known knowledge in existing clinical AD studies.ConclusionAll these promising experimental results have demonstrated the effectiveness of our new method.SignificanceWe anticipate that our new method is of interest to biomedical engineering communities beyond AD research and have open-sourced the code of our method online.
DOI：
10.1609/aaai.v31i1.10946
发表时间：
2017-02
期刊：
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影响因子：
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作者：
Yun Liu;Yiming Guo;Hua Wang;F. Nie;Heng Huang
通讯作者：
Yun Liu;Yiming Guo;Hua Wang;F. Nie;Heng Huang
通过高阶多模态多任务特征学习预测认知结果的进展
DOI：
10.1109/isbi.2018.8363635
发表时间：
2018
期刊：
The Proceedings of IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018
影响因子：
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作者：
Lu, Lyujian;Wang, Hua;Yao, Xiaohui;Risacher, Shannon;Saykin, Andrew;Shen, Li
通讯作者：
Shen, Li