White matter connectivity networks predict levodopa-induced dyskinesia in Parkinson’s disease

• Published in: Journal of neurology 2022-06, Vol.269 (6), p.2948-2960
• Main Authors: Jung, Jin Ho, Kim, Yae Ji, Chung, Seok Jong, Yoo, Han Soo, Lee, Yang Hyun, Baik, Kyoungwon, Jeong, Seong Ho, Lee, Young Gun, Lee, Hye Sun, Ye, Byoung Seok, Sohn, Young H., Jeong, Yong, Lee, Phil Hyu
• Format: Article
• Language: English
• Subjects: Antiparkinson Agents - adverse effects; Comparative analysis; Dyskinesia; Dyskinesia, Drug-Induced - diagnostic imaging; Dyskinesia, Drug-Induced - etiology; Frontal gyrus; Humans; Latency; Levodopa; Levodopa - adverse effects; Magnetic resonance imaging; Medicine; Medicine & Public Health; Movement disorders; Neural networks; Neurodegenerative diseases; Neurology; Neuroradiology; Neurosciences; Original Communication; Parkinson Disease - drug therapy; Parkinson's disease; Precentral gyrus; Putamen; Risk factors; Statistics; Substantia alba; Thalamus; White Matter - diagnostic imaging
• ISSN: 0340-5354; 1432-1459; 1432-1459
• DOI: 10.1007/s00415-021-10883-1

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.