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Parkinson’s Disease
Volume 2016, Article ID 8704910, 10 pages
Research Article

Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity

1Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
2School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
3Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, Suwon 16419, Republic of Korea

Received 28 October 2015; Revised 3 February 2016; Accepted 10 February 2016

Academic Editor: Carlo Colosimo

Copyright © 2016 Mansu Kim and Hyunjin Park. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. It is critical to distinguish between Parkinson’s disease (PD) and scans without evidence of dopaminergic deficit (SWEDD), because the two groups are different and require different therapeutic approaches. Objective. The aim of this study was to distinguish SWEDD patients from PD patients using connectivity information derived from diffusion tensor imaging tractography. Methods. Diffusion magnetic resonance images of SWEDD () and PD () were obtained from a research database. Tractography, the process of obtaining neural fiber information, was performed using custom software. Group-wise differences between PD and SWEDD patients were quantified using the number of connected fibers between two regions, and correlation analyses were performed based on clinical scores. A support vector machine classifier (SVM) was applied to distinguish PD and SWEDD based on group-wise differences. Results. Four connections showed significant group-wise differences and correlated with the Unified Parkinson’s Disease Rating Scale sponsored by the Movement Disorder Society. The SVM classifier attained 77.92% accuracy in distinguishing between SWEDD and PD using these identified connections. Conclusions. The connections and regions identified represent candidates for future research investigations.