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Non-motor Disorders in Parkinson’s Disease and Other Parkinsonian Syndromes, 2nd...
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Non-motor Disorders in Parkinson’s Disease and Other Parkinsonian Syndromes, 2nd Edition

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Neurology".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3886

Special Issue Editor

Dr. Anastasia Bougea

E-Mail Website
Guest Editor
1st Department of Neurology, Memory & Movement Disorder Clinic, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
Interests: Parkinsonian syndromes; dementias and biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Parkinson’s disease (PD) is the second most common multi-systemic neurodegenerative disorder that is characterized by a broad spectrum of motor and non-motor symptoms (NMS). Atypical Parkinsonism is a less common group of sporadic, neurodegenerative diseases of the central nervous system but more severe than PD. The most common forms are multiple system atrophy (MSA), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and dementia with Lewy bodies (DLB). Neuroanatomically, NMS may be subdivided into cortical manifestations (psychosis and cognitive impairment), basal ganglia symptoms (impulse control disorders, apathy, and restlessness or akathisia), brainstem symptoms (depression, anxiety, and sleep disorders), and peripheral nervous system disturbances (orthostatic hypotension (OH), constipation, pain), and sensory disturbances. NMS is often overlooked by physicians and dismissed by patients, making its management difficult and a major burden for patients and caregivers. Unfortunately, there is very little existing data about NMS, their neurobiology, their potential biomarkers, their monitoring, and their treatment. Moreover, there is growing evidence of accurate monitoring of NMS by wearable sensors for PD. It is, therefore, essential that researchers and practitioners comprehensively address the factors related to NMS in order to improve the quality of life for PD patients.

The aim of the 2nd edition of this Special Issue is to welcome back research articles, opinion/perspective articles, and review articles (narrative reviews, systematic reviews, and meta-analyses), as well as preclinical studies with animal models.

Dr. Anastasia Bougea
Guest Editor

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Parkinson’s disease (PD)
  • non-motor symptoms
  • atypical Parkinsonism
  • genetics
  • biomarkers
  • wearable sensors

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Published Papers (3 papers)

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Research

11 pages, 1598 KiB  
Article
Sleep Disturbances and Pain Subtypes in Parkinson’s Disease
by Stefania Diaconu, Bianca Ciopleias, Anca Zarnoveanu and Cristian Falup-Pecurariu
Medicina 2025, 61(4), 591; https://doi.org/10.3390/medicina61040591 - 26 Mar 2025
Viewed by 313
Abstract
Background and Objectives: Sleep and pain are non-motor symptoms encountered frequently in Parkinson’s disease (PD). Several subtypes of pain have been identified in PD, with different associations with other non-motor symptoms. To evaluate the prevalence of various subtypes of pain in a [...] Read more.
Background and Objectives: Sleep and pain are non-motor symptoms encountered frequently in Parkinson’s disease (PD). Several subtypes of pain have been identified in PD, with different associations with other non-motor symptoms. To evaluate the prevalence of various subtypes of pain in a PD cohort and their associations with sleep disturbances and quality of sleep. Materials and Methods: In this study, 131 consecutive PD patients were assessed, focusing on pain and sleep using several validated scales and questionnaires. Results: According to KPPQ, the most reported types of pain were musculoskeletal pain (82.44%), nocturnal pain (58.77%), and radicular pain (55.72%). “Bad sleepers” (PSQI score > 5) reported significantly more pain than “good sleepers” regarding all KPPS subdomains, with statistically significant differences observed in the following domains: musculoskeletal pain (5.48 ± 3.50 vs. 2.70 ± 2.67, p < 0.001), chronic pain, specifically central pain (1.19 ± 2.01 vs. 0.15 ± 0.71, p = 0.004), nocturnal pain, specifically pain related to akinesia (2.26 ± 2.74 vs. 0.64 ± 1.22, p = 0.001), and radicular pain (4.35 ± 4.20 vs. 2.45 ± 3.55, p = 0.022). The prevalence of sleep disturbances was higher in patients with nocturnal pain (odds = 1.165, 95% CI: 1.064–1.276, p = 0.001), orofacial pain (odds = 1.108, 95% CI: 1.051–1.167, p < 0.001), and radicular pain (odds = 1.015, 95% CI: 1.015–1.149, p = 0.015). Conclusions: Pain is common in PD patients with sleep disorders. Identifying specific types of pain that are associated with sleep disorders and their correct management may improve sleep quality. Full article
(This article belongs to the Special Issue Non-motor Disorders in Parkinson’s Disease and Other Parkinsonian Syndromes, 2nd Edition)
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8 pages, 271 KiB  
Article
Immersive Virtual Reality as Computer-Assisted Cognitive–Motor Dual-Task Training in Patients with Parkinson’s Disease
by Lucie Honzíková, Marcela Dąbrowská, Irena Skřinařová, Kristýna Mullerová, Renáta Čecháčková, Eva Augste, Jana Trdá, Šárka Baníková, Michal Filip, David Školoudík, Iva Štefková and Vojtěch Štula
Medicina 2025, 61(2), 248; https://doi.org/10.3390/medicina61020248 - 1 Feb 2025
Viewed by 1028
Abstract
Background and Objectives: The aim of this study was to determine the effect of immersive virtual reality used as a short-term multifaceted activity with a focus on motor and cognitive function in patients with Parkinson’s Disease. The sub-objective focused on quality of [...] Read more.
Background and Objectives: The aim of this study was to determine the effect of immersive virtual reality used as a short-term multifaceted activity with a focus on motor and cognitive function in patients with Parkinson’s Disease. The sub-objective focused on quality of life in the study group of patients. Materials and Methods: Nineteen patients (64.2 ± 12.8 years) were included in this study. Inclusion criteria for this study: adult patients in Hoehn and Yahr’s stage 1–3, cooperative, with stable health status, independent and mobile. IVR therapy was performed twice a week for 20 min for one month. Input and output measurements were taken within 14 days of starting or ending therapy. The 10 Meter Walk test was used to examine and assess both comfortable and fast walking, and the Timed Up and Go (TUG) + s dual task was applied to quickly assess the highest possible level of functional mobility. The Berg Balance Scale test (BBS) was used to assess balance with a 14-item balance scale containing specific movement tasks. The standardized Parkinson’s Disease Questionnaire (PDQ-39) was used to assess quality of life. Data were processed in the PAST program using a nonparametric paired Wilcoxon test. The significance level was set at α = 0.05. The value of the r score was used to evaluate the effect size. Results: A significant reduction in the time in the fast walk 10MWT (p = 0.006; r = 0.63) and TUG (p < 0.001; r = 0.80) parameter were found after therapy. Significant improvement in the BBS score was found after applied therapy (p = 0.016; r = 0.55). In the PDQ-39 questionnaire, significant improvements were found in the study group after therapy in the domains of mobility (p = 0.027; r = 0.51) and emotional well-being (p = 0.011; r = 0.58). Conclusions: The results of this study indicate a positive effect of virtual reality therapy on balance and gait, which is also good in terms of reducing the risk of falls in the study group. Therapy also promoted quality of life in the study group. Full article
(This article belongs to the Special Issue Non-motor Disorders in Parkinson’s Disease and Other Parkinsonian Syndromes, 2nd Edition)
12 pages, 983 KiB  
Article
An Artificial Neural Network Predicts Gender Differences of Motor and Non-Motor Symptoms of Patients with Advanced Parkinson’s Disease under Levodopa–Carbidopa Intestinal Gel
by Anastasia Bougea, Tajedin Derikvand and Efthymia Efthimiopoulou
Medicina 2024, 60(6), 873; https://doi.org/10.3390/medicina60060873 - 26 May 2024
Viewed by 1792
Abstract
Background and Objectives: Currently, no tool exists to predict clinical outcomes in patients with advanced Parkinson’s disease (PD) under levodopa–carbidopa intestinal gel (LCIG) treatment. The aim of this study was to develop a novel deep neural network model to predict the clinical [...] Read more.
Background and Objectives: Currently, no tool exists to predict clinical outcomes in patients with advanced Parkinson’s disease (PD) under levodopa–carbidopa intestinal gel (LCIG) treatment. The aim of this study was to develop a novel deep neural network model to predict the clinical outcomes of patients with advanced PD after two years of LCIG therapy. Materials and Methods: This was a longitudinal, 24-month observational study of 59 patients with advanced PD in a multicenter registry under LCIG treatment from September 2019 to September 2021, including 43 movement disorder centers. The data set includes 649 measurements of patients, which make an irregular time series, and they are turned into regular time series during the preprocessing phase. Motor status was assessed with the Unified Parkinson’s Disease Rating Scale (UPDRS) Parts III (off) and IV. The NMS was assessed by the NMS Questionnaire (NMSQ) and the Geriatric Depression Scale (GDS), the quality of life by PDQ-39, and severity by Hoehn and Yahr (HY). Multivariate linear regression, ARIMA, SARIMA, and Long Short-Term Memory–Recurrent NeuralNetwork (LSTM-RNN) models were used. Results: LCIG significantly improved dyskinesia duration and quality of life, with men experiencing a 19% and women a 10% greater improvement, respectively. Multivariate linear regression models showed that UPDRS-III decreased by 1.5 and 4.39 units per one-unit increase in the PDQ-39 and UPDRS-IV indexes, respectively. Although the ARIMA-(2,0,2) model is the best one with AIC criterion 101.8 and validation criteria MAE = 0.25, RMSE = 0.59, and RS = 0.49, it failed to predict PD patients’ features over a long period of time. Among all the time series models, the LSTM-RNN model predicts these clinical characteristics with the highest accuracy (MAE = 0.057, RMSE = 0.079, RS = 0.0053, mean square error = 0.0069). Conclusions: The LSTM-RNN model predicts, with the highest accuracy, gender-dependent clinical outcomes in patients with advanced PD after two years of LCIG therapy. Full article
(This article belongs to the Special Issue Non-motor Disorders in Parkinson’s Disease and Other Parkinsonian Syndromes, 2nd Edition)
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