Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity
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 patient...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
|
| Series: | Parkinson's Disease |
| Online Access: | http://dx.doi.org/10.1155/2016/8704910 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849306828180029440 |
|---|---|
| author | Mansu Kim Hyunjin Park |
| author_facet | Mansu Kim Hyunjin Park |
| author_sort | Mansu Kim |
| collection | DOAJ |
| description | 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 (n=37) and PD (n=40) 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. |
| format | Article |
| id | doaj-art-1e848afd9d2d48a2ba253a7156cebf76 |
| institution | Kabale University |
| issn | 2090-8083 2042-0080 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Parkinson's Disease |
| spelling | doaj-art-1e848afd9d2d48a2ba253a7156cebf762025-08-20T03:54:57ZengWileyParkinson's Disease2090-80832042-00802016-01-01201610.1155/2016/87049108704910Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on ConnectivityMansu Kim0Hyunjin Park1Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaSchool of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaBackground. 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 (n=37) and PD (n=40) 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.http://dx.doi.org/10.1155/2016/8704910 |
| spellingShingle | Mansu Kim Hyunjin Park Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity Parkinson's Disease |
| title | Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity |
| title_full | Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity |
| title_fullStr | Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity |
| title_full_unstemmed | Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity |
| title_short | Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity |
| title_sort | using tractography to distinguish swedd from parkinson s disease patients based on connectivity |
| url | http://dx.doi.org/10.1155/2016/8704910 |
| work_keys_str_mv | AT mansukim usingtractographytodistinguishsweddfromparkinsonsdiseasepatientsbasedonconnectivity AT hyunjinpark usingtractographytodistinguishsweddfromparkinsonsdiseasepatientsbasedonconnectivity |