National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055 China.
CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine, School of Computing and the School of Medicine, University of Leeds, Leeds LS2 9JT, United Kingdom.
Med Image Anal. 2020 Apr;61:101652. doi: 10.1016/j.media.2020.101652. Epub 2020 Jan 17.
Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonly used to distinguish MCI patients from normal controls (NC). However, existing methods still suffer from limited performance, and classification remains mainly based on single modality data. This paper proposes a new model to automatically diagnosing MCI (early MCI (EMCI) and late MCI (LMCI)) and its earlier stages (i.e., significant memory concern (SMC)) by combining low-rank self-calibrated functional brain networks and structural brain networks for joint multi-task learning. Specifically, we first develop a new functional brain network estimation method. We introduce data quality indicators for self-calibration, which can improve data quality while completing brain network estimation, and perform correlation analysis combined with low-rank structure. Second, functional and structural connected neuroimaging patterns are integrated into our multi-task learning model to select discriminative and informative features for fine MCI analysis. Different modalities are best suited to undertake distinct classification tasks, and similarities and differences among multiple tasks are best determined through joint learning to determine most discriminative features. The learning process is completed by non-convex regularizer, which effectively reduces the penalty bias of trace norm and approximates the original rank minimization problem. Finally, the most relevant disease features classified using a support vector machine (SVM) for MCI identification. Experimental results show that our method achieves promising performance with high classification accuracy and can effectively discriminate between different sub-stages of MCI.
阿尔茨海默病（AD）早期阶段（即轻度认知障碍（MCI））的检测对于最大程度地提高延缓或预防向 AD 进展的机会非常重要。从医学成像数据推断出的大脑连通性网络已被广泛用于区分 MCI 患者和正常对照组（NC）。然而，现有的方法仍然存在性能有限的问题，分类仍然主要基于单一模态数据。本文提出了一种新的模型，通过结合低秩自校准功能脑网络和结构脑网络进行联合多任务学习，自动诊断 MCI（早期 MCI（EMCI）和晚期 MCI（LMCI））及其早期阶段（即显著记忆问题（SMC））。具体来说，我们首先开发了一种新的功能脑网络估计方法。我们引入了用于自校准的数据质量指标，这可以在完成脑网络估计的同时提高数据质量，并结合低秩结构进行相关分析。其次，功能和结构连接神经影像学模式被整合到我们的多任务学习模型中，以选择用于精细 MCI 分析的有区别和信息丰富的特征。不同的模态最适合承担不同的分类任务，通过联合学习可以最好地确定多个任务之间的相似性和差异，以确定最具区分性的特征。学习过程由非凸正则化完成，有效地减少了迹范数的惩罚偏差，并近似于原始秩最小化问题。最后，使用支持向量机（SVM）对 MCI 识别进行分类，选择最相关的疾病特征。实验结果表明，我们的方法具有很高的分类精度，能够有效地区分 MCI 的不同亚期，具有很有前景的性能。
