A ReliefF-SVM-based method for marking dopamine-based disease characteristics: A study on SWEDD and Parkinson's disease

被引:17
作者
Jin, Liling [1 ]
Zeng, Qingrun [1 ]
He, Jianzhong [1 ]
Feng, Yuanjing [1 ]
Zhou, Siqi [1 ]
Wu, Ye [1 ]
机构
[1] ZheJiang Univ Technol China, Sch Informat Engn, Inst Informat Proc & Automat, Hangzhou 310023, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
dMRI; Tractography; Machine learning; Parkinson's disease; SWEDD; IMAGING BIOMARKERS; WHITE-MATTER; MACHINE; CLASSIFICATION; DIAGNOSIS; MRI; SCANS; CONSISTENCY; NETWORKS;
D O I
10.1016/j.bbr.2018.09.003
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
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.
引用
收藏
页码:400 / 407
页数:8
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