Dataset Information

"Cueing" for Levodopa-Induced Dyskinesias in Parkinson's Disease.

ABSTRACT: We describe the case of an early-onset Parkinson's disease (PD) patient, suffering from a severe and unusual dyskinetic gait pattern, earlier described as "Silly walk." On presentation, the 58-year-old patient showed a painful, bizarre dyskinetic gait disorder, resulting in a significantly impairment of her social life. We developed an individual conservative training method for the patient, using "Cueing mechanisms," well known for treatment of Freezing in PD, to overcome her dyskinetic gait pattern. An impressive improvement was seen after the use of visual and acousting cues. We, therefore, conclude that it might be important for PD patients to recognize these specific movement abnormalities and start early with individualized training methods. Moreover, especially for "Silly walk" individuals, "Cueing" strategies can be an important new basis of conservative training methods for dyskinesia.

SUBMITTER: Schaeffer E

PROVIDER: S-EPMC5179554 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

"Cueing" for Levodopa-Induced Dyskinesias in Parkinson's Disease.

Schaeffer Eva E Linke Gerd G Berg Daniela D

Frontiers in neurology 20161223

We describe the case of an early-onset Parkinson's disease (PD) patient, suffering from a severe and unusual dyskinetic gait pattern, earlier described as "Silly walk." On presentation, the 58-year-old patient showed a painful, bizarre dyskinetic gait disorder, resulting in a significantly impairment of her social life. We developed an individual conservative training method for the patient, using "Cueing mechanisms," well known for treatment of Freezing in PD, to overcome her dyskinetic gait pa ...[more]

PMID: 28066321

Similar Datasets

Project description:Background: Adaptive Deep Brain Stimulation (aDBS) is now considered as a new feasible and effective paradigm to deliver DBS to patients with Parkinson's disease (PD) in such a way that not only stimulation is personalized and finely tuned to the instantaneous patient's state, but also motor improvement is obtained with a lower amount of energy transferred to the tissue. Amplitude-controlled aDBS was shown to significantly decrease the amplitude-driven total electrical energy delivered to the tissue (aTEED), an objective measure of the amount of energy transferred by DBS amplitude to the patient's brain. However, there is no direct evidence of a relationship between aTEED and the occurrence of DBS-related adverse events in humans. Objective: In this work, we investigated the correlation of aTEED with the occurrence of levodopa-induced dyskinesias pooling all the data available from our previous experiments using aDBS and cDBS. Methods: We retrospectively analyzed data coming from 19 patients with PD undergoing surgery for STN-DBS electrode positioning and participating to experiments involving cDBS and aDBS delivery. Patients were all studied some days after the surgery (acute setting). The aTEED and dyskinesia assessments (Rush Dyskinesia Rating Scale, RDRS) considered in the Med ON-Stim ON condition. Results: We confirmed both that aTEED values and RDRS were significantly lower in the aDBS than in cDBS sessions (aTEED mean value, cDBS: 0.0278 ± 0.0011 j, vs. aDBS: 0.0071 ± 0.0003 j, p < 0.0001 Wilcoxon's rank sum; normalized RDRS mean score, cDBS: 0.66 ± 0.017 vs. aDBS: 0.45 ± 0.01, p = 0.025, Wilcoxon's rank sum test). In addition, we found a direct significant correlation between aTEED and RDRS (ρ = 0.44, p = 0.0032, Spearman's correlation). Conclusions: Our results provide a first piece of evidence that aTEED is correlated to the amount of levodopa-induced dyskinesias in patients with PD undergoing STN-DBS, thus supporting the role of aDBS as feasible and safe alternative to cDBS.

Dataset Information

"Cueing" for Levodopa-Induced Dyskinesias in Parkinson's Disease.

Publications

"Cueing" for Levodopa-Induced Dyskinesias in Parkinson's Disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets