Jin Liling, Zeng Qingrun, He Jianzhong, Feng Yuanjing, Zhou Siqi, Wu Ye
Institute of Information Processing and Automation, School of Information Engineering, ZheJiang University of Technology of China, Hangzhou 310023, China.
Institute of Information Processing and Automation, School of Information Engineering, ZheJiang University of Technology of China, Hangzhou 310023, China.
Behav Brain Res. 2019 Jan 1;356:400-407. doi: 10.1016/j.bbr.2018.09.003. Epub 2018 Sep 9.
Parkinson's disease (PD) and scans without evidence of dopaminergic deficit (SWEDD) are two distinct neurological disorders that require different therapeutic approaches; therefore it's critical to classify the two disorders. The neuroimaging technology based on dMRI provided connectivity information and voxel features that can make it possible for researchers to analyze SWEDD and PD differences. In this work, a novel method of ReliefF-SVM-based dMRI analysis was presented to study the potential relations between PD and SWEDD. Some sensorimotor connections were found group-wise differences, and SVM was suggested to successfully classify PD and SWEDD. These results indicate that our method using connectivity information and voxel features may provide a new strategy for disease analysis with small sample data.
帕金森病（PD）和无多巴胺能缺陷证据的扫描（SWEDD）是两种不同的神经系统疾病，需要不同的治疗方法；因此，对这两种疾病进行分类至关重要。基于扩散磁共振成像（dMRI）的神经成像技术提供了连接信息和体素特征，使研究人员能够分析SWEDD和PD之间的差异。在这项工作中，提出了一种基于ReliefF-支持向量机（SVM）的dMRI分析新方法，以研究PD和SWEDD之间的潜在关系。发现一些感觉运动连接存在组间差异，并表明支持向量机能够成功区分PD和SWEDD。这些结果表明，我们利用连接信息和体素特征的方法可能为小样本数据的疾病分析提供一种新策略。
