IEEE J Biomed Health Inform. 2020 Apr;24(4):1160-1168. doi: 10.1109/JBHI.2019.2934230. Epub 2019 Aug 9.
Mild cognitive impairment (MCI) is an intermediate stage of brain cognitive decline, associated with increasing risk of developing Alzheimer's disease (AD). It is believed that early treatment of MCI could slow down the progression of AD, and functional brain network (FBN) could provide potential imaging biomarkers for MCI diagnosis and response to treatment. However, there are still some challenges to estimate a "good" FBN, particularly due to the poor quality and limited quantity of functional magnetic resonance imaging (fMRI) data from the target domain (i.e., MCI study). Inspired by the idea of transfer learning, we attempt to transfer information in high-quality data from source domain (e.g., human connectome project in this paper) into the target domain towards a better FBN estimation, and propose a novel method, namely NERTL (Network Estimation via Regularized Transfer Learning). Specifically, we first construct a high-quality network "template" based on the source data, and then use the template to guide or constrain the target of FBN estimation by a weighted l-norm regularizer. Finally, we conduct experiments to identify subjects with MCI from normal controls (NCs) based on the estimated FBNs. Despite its simplicity, our proposed method is more effective than the baseline methods in modeling discriminative FBNs, as demonstrated by the superior MCI classification accuracy of 82.4% and the area under curve (AUC) of 0.910.
轻度认知障碍 (MCI) 是大脑认知能力下降的中间阶段，与阿尔茨海默病 (AD) 的发病风险增加有关。据信，早期治疗 MCI 可以减缓 AD 的进展，而功能脑网络 (FBN) 可以为 MCI 的诊断和治疗反应提供潜在的影像学生物标志物。然而，估计一个“良好”的 FBN 仍然存在一些挑战，特别是由于目标领域（即 MCI 研究）的功能磁共振成像 (fMRI) 数据质量差和数量有限。受迁移学习思想的启发，我们试图将源域（例如本文中的人类连接组计划）中的高质量数据中的信息转移到目标域中，以更好地估计 FBN，并提出了一种新方法，即 NERTL（通过正则化迁移学习进行网络估计）。具体来说，我们首先基于源数据构建一个高质量的网络“模板”，然后使用模板通过加权 l-范数正则化器引导或约束 FBN 估计的目标。最后，我们进行了实验，根据估计的 FBN 从正常对照组 (NC) 中识别出 MCI 患者。尽管我们的方法很简单，但与基线方法相比，它在建模有区别的 FBN 方面更有效，这体现在 82.4%的 MCI 分类准确率和 0.910 的 AUC 上。
