ArticleJournal of neurology2022
White matter connectivity networks predict levodopa-induced dyskinesia in Parkinson's disease.
Article in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Plasma neurofilament light chain in early Parkinson's disease predicts motor complications: a prospective cohort study.NPJ Parkinson's disease · 2026Article
- Feasibility study of differentiating patients with levodopa-induced dyskinesia using cerebellar gray and white matter radiomics features from 3DT1WI images.Frontiers in aging neuroscience · 2026Article
- Article
- Association between striatal amyloid deposition and motor prognosis in Parkinson's disease.European journal of neurology · 2024Article
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundAlthough 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.
objectiveTo elucidate the role of white matter connectivity networks in the development of levodopa-induced dyskinesia in drug-naïve Parkinson's disease.
methodsWe 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.
resultsMean-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.
conclusionsOur data suggest that white matter connectivity subnetworks within corticostriatal regions play a pivotal role in the development of levodopa-induced dyskinesia.
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