Zhang Yu, Zhang Han, Adeli Ehsan, Chen Xiaobo, Liu Mingxia, Shen Dinggang
IEEE Trans Cybern. 2022 Jul;52(7):6822-6833. doi: 10.1109/TCYB.2020.3016953. Epub 2022 Jul 4.
Functional connectivity (FC) networks built from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results for the diagnosis of Alzheimer's disease and its prodromal stage, that is, mild cognitive impairment (MCI). FC is usually estimated as a temporal correlation of regional mean rs-fMRI signals between any pair of brain regions, and these regions are traditionally parcellated with a particular brain atlas. Most existing studies have adopted a predefined brain atlas for all subjects. However, the constructed FC networks inevitably ignore the potentially important subject-specific information, particularly, the subject-specific brain parcellation. Similar to the drawback of the "single view" (versus the "multiview" learning) in medical image-based classification, FC networks constructed based on a single atlas may not be sufficient to reveal the underlying complicated differences between normal controls and disease-affected patients due to the potential bias from that particular atlas. In this study, we propose a multiview feature learning method with multiatlas-based FC networks to improve MCI diagnosis. Specifically, a three-step transformation is implemented to generate multiple individually specified atlases from the standard automated anatomical labeling template, from which a set of atlas exemplars is selected. Multiple FC networks are constructed based on these preselected atlas exemplars, providing multiple views of the FC network-based feature representations for each subject. We then devise a multitask learning algorithm for joint feature selection from the constructed multiple FC networks. The selected features are jointly fed into a support vector machine classifier for multiatlas-based MCI diagnosis. Extensive experimental comparisons are carried out between the proposed method and other competing approaches, including the traditional single-atlas-based method. The results indicate that our method significantly improves the MCI classification, demonstrating its promise in the brain connectome-based individualized diagnosis of brain diseases.
基于静息态功能磁共振成像（rs-fMRI）构建的功能连接（FC）网络在阿尔茨海默病及其前驱期即轻度认知障碍（MCI）的诊断中已显示出有前景的结果。FC通常被估计为任意一对脑区之间区域平均rs-fMRI信号的时间相关性，并且这些区域传统上是用特定的脑图谱进行划分的。大多数现有研究对所有受试者都采用了预定义的脑图谱。然而，构建的FC网络不可避免地忽略了潜在重要的个体特异性信息，特别是个体特异性的脑分区。类似于基于医学图像的分类中“单视图”（相对于“多视图”学习）的缺点，基于单个图谱构建的FC网络可能不足以揭示正常对照和疾病患者之间潜在的复杂差异，因为来自该特定图谱的潜在偏差。在本研究中，我们提出一种基于多图谱的FC网络的多视图特征学习方法来改善MCI诊断。具体而言，实施三步变换以从标准自动解剖标记模板生成多个单独指定的图谱，从中选择一组图谱范例。基于这些预选的图谱范例构建多个FC网络，为每个受试者提供基于FC网络的特征表示的多个视图。然后，我们设计一种多任务学习算法，用于从构建的多个FC网络中进行联合特征选择。所选特征被联合输入到基于多图谱的MCI诊断的支持向量机分类器中。在所提出的方法与其他竞争方法（包括传统的基于单图谱的方法）之间进行了广泛的实验比较。结果表明，我们的方法显著提高了MCI分类，证明了其在基于脑连接组的脑部疾病个体化诊断中的前景。
