Yan Jingwen, Huang Heng, Risacher Shannon L, Kim Sungeun, Inlow Mark, Moore Jason H, Saykin Andrew J, Shen Li
Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
School of Informatics, Indiana University Indianapolis, IN, USA.
Multimodal Brain Image Anal (2013). 2013;8159:202-210. doi: 10.1007/978-3-319-02126-3_20.
Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. In particular, sparse models have been proposed to identify the optimal imaging markers with high prediction power. However, the complex relationship among imaging markers are often overlooked or simplified in the existing methods. To address this issue, we present a new sparse learning method by introducing a novel network term to more flexibly model the relationship among imaging markers. The proposed algorithm is applied to the ADNI study for predicting cognitive outcomes using MRI scans. The effectiveness of our method is demonstrated by its improved prediction performance over several state-of-the-art competing methods and accurate identification of cognition-relevant imaging markers that are biologically meaningful.
阿尔茨海默病（AD）的特征是逐渐发生神经退行性变和脑功能丧失，尤其是在早期阶段记忆功能丧失。回归分析已广泛应用于AD研究，以关联临床和生物标志物数据，如从MRI测量预测认知结果。特别是，已经提出了稀疏模型来识别具有高预测能力的最佳成像标志物。然而，现有方法往往忽视或简化了成像标志物之间的复杂关系。为了解决这个问题，我们提出了一种新的稀疏学习方法，通过引入一个新颖的网络项来更灵活地建模成像标志物之间的关系。所提出的算法应用于ADNI研究，以使用MRI扫描预测认知结果。我们的方法通过优于几种最先进的竞争方法的预测性能以及准确识别具有生物学意义的与认知相关的成像标志物，证明了其有效性。
