Li Jialiang, Yao Zhaomin, Duan Meiyu, Liu Shuai, Li Fei, Zhu Haiyang, Xia Zhiqiang, Huang Lan, Zhou Fengfeng
BioKnow Health Informatics Laboratory, College of Software, Jilin University, Changchun 130012, China.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
IEEE Access. 2020;8:174023-174031. doi: 10.1109/access.2020.3025828. Epub 2020 Sep 22.
The neurological disorder mild cognitive impairment (MCI) demonstrates minor impacts on the patient's daily activities and may be ignored as the status of normal aging. But some of the MCI patients may further develop into severe statuses like Alzheimer's disease (AD). The brain functional connectivity network (BFCN) was usually constructed from the resting-state functional magnetic resonance imaging (rs-fMRI) data. This technology has been widely used to detect the neurodegenerative dementia and to reveal the intrinsic mechanism of neural activities. The BFCN edge was usually determined by the pairwise correlation between the brain regions. This study proposed a weighted voting model of multi-source connectivity networks (MuscNet) by integrating multiple BFCNs of different correlation coefficients. Our model was further improved by removing redundant features. The experimental data demonstrated that different BFCNs contributed complementary information to each other and MuscNet outperformed the existing models on detecting MCI patients. The previous study suggested the existence of multiple solutions with similarly good performance for a machine learning problem. The proposed model MuscNet utilized a weighted voting strategy to slightly outperform the existing studies, suggesting an effective way to fuse multiple base models. The reason may need further theoretical investigations about why different base models contribute to each other for the MCI prediction.
神经疾病轻度认知障碍（MCI）对患者的日常活动影响较小，可能会被视为正常衰老状态而被忽视。但部分MCI患者可能会进一步发展为严重状态，如阿尔茨海默病（AD）。脑功能连接网络（BFCN）通常由静息态功能磁共振成像（rs-fMRI）数据构建。该技术已被广泛用于检测神经退行性痴呆并揭示神经活动的内在机制。BFCN的边通常由脑区之间的成对相关性确定。本研究通过整合不同相关系数的多个BFCN，提出了一种多源连接网络加权投票模型（MuscNet）。通过去除冗余特征，我们的模型得到了进一步改进。实验数据表明，不同的BFCN相互提供互补信息，并且MuscNet在检测MCI患者方面优于现有模型。先前的研究表明，对于一个机器学习问题，存在多个性能同样良好的解决方案。所提出的MuscNet模型采用加权投票策略，略优于现有研究，这表明了一种融合多个基础模型的有效方法。对于不同基础模型为何在MCI预测中相互贡献，其原因可能需要进一步的理论研究。
