Jin Ho Jung1, Yae Ji Kim2,3, Seok Jong Chung4,5, Han Soo Yoo4, Yang Hyun Lee4, Kyoungwon Baik4, Seong Ho Jeong6, Young Gun Lee4, Hye Sun Lee7, Byoung Seok Ye4, Young H Sohn4, Yong Jeong8,9,10, Phil Hyu Lee11,12. 1. Department of Neurology, Busan Paik Hospital, Inje University College of Medicine, Busan, South Korea. 2. Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. 3. KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. 4. Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea. 5. Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, South Korea. 6. Department of Neurology, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, South Korea. 7. Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, South Korea. 8. Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. yong@kaist.ac.kr. 9. KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. yong@kaist.ac.kr. 10. Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. yong@kaist.ac.kr. 11. Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea. phlee@yuhs.ac. 12. Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, South Korea. phlee@yuhs.ac.
Abstract
BACKGROUND: Although levodopa-induced dyskinesia-relevant white matter change has been evaluated, it is uncertain whether these changes may reflect the underlying predisposing conditions leading to the development of levodopa-induced dyskinesia. OBJECTIVE: To elucidate the role of white matter connectivity networks in the development of levodopa-induced dyskinesia in drug-naïve Parkinson's disease. METHODS: We recruited 30 patients who developed levodopa-induced dyskinesia within 5 years from MRI acquisition (vulnerable-group), 47 patients who had not developed levodopa-induced dyskinesia within 5 years (resistant-group), and 28 controls. We performed comparative analyses of whole-brain white matter integrity and connectivity using tract-based spatial and network- and degree-based statistics. We evaluated the predictability of levodopa-induced dyskinesia development and relationship with its latency, using the average connectivity strength as a predictor in Cox- and linear-regression, respectively. RESULTS: Mean-diffusivity was lower mainly at the left frontal region in the vulnerable-group compared to the resistant-group. Network-based statistics identified a subnetwork consisting of the bilateral fronto-striato-pallido-thalamic and lateral parietal regions (subnetwork A) and degree-based statistics identified four subnetworks (hub-subnetwork) consisting of edges centered on the left superior frontal gyrus, left putamen, left insular, or left precentral gyrus, where the vulnerable-group had stronger connectivity compared to the resistant-group. Stronger connectivity within the subnetwork A and hub-subnetwork centered on the left superior frontal gyrus was a predictor of levodopa-induced dyskinesia development independent of known risk factors and had an inverse relationship with its latency. CONCLUSIONS: Our data suggest that white matter connectivity subnetworks within corticostriatal regions play a pivotal role in the development of levodopa-induced dyskinesia.
BACKGROUND: Although levodopa-induced dyskinesia-relevant white matter change has been evaluated, it is uncertain whether these changes may reflect the underlying predisposing conditions leading to the development of levodopa-induced dyskinesia. OBJECTIVE: To elucidate the role of white matter connectivity networks in the development of levodopa-induced dyskinesia in drug-naïve Parkinson's disease. METHODS: We recruited 30 patients who developed levodopa-induced dyskinesia within 5 years from MRI acquisition (vulnerable-group), 47 patients who had not developed levodopa-induced dyskinesia within 5 years (resistant-group), and 28 controls. We performed comparative analyses of whole-brain white matter integrity and connectivity using tract-based spatial and network- and degree-based statistics. We evaluated the predictability of levodopa-induced dyskinesia development and relationship with its latency, using the average connectivity strength as a predictor in Cox- and linear-regression, respectively. RESULTS: Mean-diffusivity was lower mainly at the left frontal region in the vulnerable-group compared to the resistant-group. Network-based statistics identified a subnetwork consisting of the bilateral fronto-striato-pallido-thalamic and lateral parietal regions (subnetwork A) and degree-based statistics identified four subnetworks (hub-subnetwork) consisting of edges centered on the left superior frontal gyrus, left putamen, left insular, or left precentral gyrus, where the vulnerable-group had stronger connectivity compared to the resistant-group. Stronger connectivity within the subnetwork A and hub-subnetwork centered on the left superior frontal gyrus was a predictor of levodopa-induced dyskinesia development independent of known risk factors and had an inverse relationship with its latency. CONCLUSIONS: Our data suggest that white matter connectivity subnetworks within corticostriatal regions play a pivotal role in the development of levodopa-induced dyskinesia.
Authors: T E J Behrens; M W Woolrich; M Jenkinson; H Johansen-Berg; R G Nunes; S Clare; P M Matthews; J M Brady; S M Smith Journal: Magn Reson Med Date: 2003-11 Impact factor: 4.668