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 l1-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.

中文翻译：

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更好地估计功能性大脑网络以识别轻度认知障碍：迁移学习观点。


轻度认知障碍 (MCI) 是大脑认知能力下降的中间阶段，与患阿尔茨海默病 (AD) 的风险增加相关。人们认为，MCI的早期治疗可以减缓AD的进展，而功能性脑网络（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“好的”FBN 仍然存在一些挑战，特别是由于目标域（即 MCI 研究）的功能磁共振成像 (fMRI) 数据质量较差且数量有限。受迁移学习思想的启发，我们尝试将高质量数据中的信息从源域（例如本文中的人类连接组项目）迁移到目标域，以实现更好的 FBN 估计，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，我们首先根据源数据构建一个高质量的网络“模板”，然后使用该模板通过加权l1范数正则化器来引导或约束FBN估计的目标。最后，我们进行实验，根据估计的 FBN 从正常对照 (NC) 中识别 MCI 受试者。尽管很简单，我们提出的方法在建模判别性 FBN 方面比基线方法更有效，82.4% 的优越 MCI 分类精度和 0.910 的曲线下面积 (AUC) 证明了这一点。