Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers
Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers
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DOI：
10.1109/cvpr42600.2020.00488
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Lyujian Lu;Hua Wang;Saad Elbeleidy;F. Nie
中科院分区：
文献类型：
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作者：
Lyujian Lu;Hua Wang;Saad Elbeleidy;F. Nie
近年来，随着高通量基因分型和神经影像学的快速发展，以阿尔茨海默病（AD）为代表的复杂脑疾病的研究受到了广泛关注。已经研究了许多预测模型，以将神经成像测量与这些疾病发展时的进展中的认知状态相关联。缺失数据是纵向神经影像学研究中准确预测受试者认知评分的最大挑战之一。为了解决这个问题，在本文中，我们提出了一种新的配方来学习成像生物标志物的丰富表示，可以同时捕获基线神经成像记录所传达的信息，以及随着时间的推移，可用的随访记录的不同计数的渐进变化。虽然参与者的大脑扫描数量各不相同，但每个参与者的学习生物标志物表示是一个固定长度的向量，这使我们能够使用传统的学习模型来研究AD的发展。我们的新目标是制定最大化的L1范数距离的总和的比率，以提高鲁棒性，虽然，这是很难有效地解决一般。从而得到一个新的有效的迭代求解算法，并严格证明了它的收敛性。我们对阿尔茨海默病神经成像倡议（ADNI）数据集进行了广泛的实验。当我们将原始基线表示与丰富的学习表示进行比较时，已经实现了预测四种不同认知分数的性能增益。这些有前途的实证结果表明，我们的新方法，验证其有效性的性能改善。
With rapid progress in high-throughput genotyping and neuroimaging, researches of complex brain disorders, such as Alzheimer’s Disease (AD), have gained significant attention in recent years. Many prediction models have been studied to relate neuroimaging measures to cognitive status over the progressions when these disease develops. Missing data is one of the biggest challenge in accurate cognitive score prediction of subjects in longitudinal neuroimaging studies. To tackle this problem, in this paper we propose a novel formulation to learn an enriched representation for imaging biomarkers that can simultaneously capture both the information conveyed by baseline neuroimaging records and that by progressive variations of varied counts of available follow-up records over time. While the numbers of the brain scans of the participants vary, the learned biomarker representation for every participant is a fixed-length vector, which enable us to use traditional learning models to study AD developments. Our new objective is formulated to maximize the ratio of the summations of a number of L1-norm distances for improved robustness, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative solution algorithm and rigorously prove its convergence. We have performed extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. A performance gain has been achieved to predict four different cognitive scores, when we compare the original baseline representations against the learned representations with enrichments. These promising empirical results have demonstrated improved performances of our new method that validate its effectiveness.