Chen Xiaobo, Zhang Han, Zhang Yu, Yang Jian, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China.
Asian Conf Pattern Recognit. 2018 Nov;2017:917-922. doi: 10.1109/ACPR.2017.147. Epub 2018 Dec 17.
Learning functional connectivity (FC) network from resting-state function magnetic resonance imaging (RS-fMRI) data via sparse representation (SR) or weighted SR (WSR) has been proved to be promising for the diagnosis of Alzheimer's disease and its prodromal stage, mild cognitive impairment (MCI). However, traditional SR/WSR based approaches learn the representation of each brain region independently, without fully taking into account the possible relationship between brain regions. To remedy this limitation, we propose a novel FC modeling approach by considering two types of possible relationship between different brain regions which are incorporated into SR/WSR approaches in the form of regularization. In this way, the representations of all brain regions can be jointly learned. Furthermore, an efficient alternating optimization algorithm is also developed to solve the resulting model. Experimental results show that our proposed method not only outperforms SR and WSR in the diagnosis of MCI subjects, but also leads to the brain FC network with better modularity structure.
通过稀疏表示（SR）或加权稀疏表示（WSR）从静息态功能磁共振成像（RS-fMRI）数据中学习功能连接（FC）网络，已被证明在阿尔茨海默病及其前驱期——轻度认知障碍（MCI）的诊断中具有前景。然而，传统的基于SR/WSR的方法独立学习每个脑区的表示，没有充分考虑脑区之间可能的关系。为了弥补这一局限性，我们提出了一种新颖的FC建模方法，通过考虑不同脑区之间两种可能的关系，并以正则化的形式将其纳入SR/WSR方法中。通过这种方式，可以联合学习所有脑区的表示。此外，还开发了一种高效的交替优化算法来求解所得模型。实验结果表明，我们提出的方法不仅在MCI受试者的诊断中优于SR和WSR，而且还能得到具有更好模块化结构的脑FC网络。
