Knowledge mapping of freezing of gait in Parkinson's disease: a bibliometric analysis.
Frontiers in neuroscience
Objective:Among the disturbing motor symptoms in Parkinson's disease (PD), freezing of gait (FOG) stands out as one of the most severe challenges. It typically arises during the initiation of gait or when turning. This phenomenon not only impose a heavy burden on patients, but also on their families. We conduct a bibliometric analysis to summarize current research hotspots and trends concerning freezing of gait in Parkinson's disease (PD-FOG) over past two decades. Methods:We retrieved articles and reviews published in English about PD-FOG in the Web of science Core Collection database from 2000 to 2023 on November 30,2023. The tools VOSviewer and CiteSpace facilitated a visual analysis covering various aspects such as publications, countries/regions, organizations, authors, journals, cited references, and keywords. Result:This study includes 1,340 articles from 64 countries/regions. There is a growth in publications related to PD-FOG over the past two decades, maintaining a stable high output since 2018, indicating a promising research landscape in the field of PD-FOG. The United States holds a leading position in this field, with Nieuwboer A and Giladi N being two of the most influential researchers. Over the past two decades, the research hotspots for PD-FOG have primarily encompassed the kinematic characteristics, diagnosis and detection, cognitive deficits and neural connectivity, as well as therapy and rehabilitation of PD-FOG. Topics including functional connectivity, virtual reality, deep learning and machine learning will be focal points of future research. Conclusion:This is the first bibliometric analysis of PD-FOG. We construct this study to summarize the research in this field over past two decades, visually show the current hotspots and trends, and offer scholars in this field concepts and strategies for subsequent studies.
10.3389/fnins.2024.1388326
The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.
Sensors (Basel, Switzerland)
Parkinson's disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increases caregivers' burdens. The use of the quantitative gait data of people with PD and deep learning (DL) approaches based on gait are emerging as increasingly promising methods to support and aid clinical decision making, with the aim of providing a quantitative and objective diagnosis, as well as an additional tool for disease monitoring. This will allow for the early detection of the disease, assessment of progression, and implementation of therapeutic interventions. In this paper, the authors provide a systematic review of emerging DL techniques recently proposed for the analysis of PD by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed, and Web of Science databases were searched across an interval of six years (between 2018, when the first article was published, and 2023). A total of 25 articles were included in this review, which reports studies on the movement analysis of PD patients using both wearable and non-wearable sensors. Additionally, these studies employed DL networks for classification, diagnosis, and monitoring purposes. The authors demonstrate that there is a wide employment in the field of PD of convolutional neural networks for analyzing signals from wearable sensors and pose estimation networks for motion analysis from videos. In addition, the authors discuss current difficulties and highlight future solutions for PD monitoring and disease progression.
10.3390/s24185957
Genetically stratified Parkinson's disease with freezing of gait is related to specific pattern of cognitive impairment and non-motor dominant endophenotype.
Frontiers in aging neuroscience
Background:Freezing of gait (FOG) is an important milestone in the individual disease trajectory of people with Parkinson's disease (PD). Based on the of FOG etiology, the mechanism behind FOG implies higher executive dysfunction in PD. To test this model, we investigated the FOG-related phenotype and cognitive subdomains in idiopathic PD (iPD) patients without genetic variants linked to PD from the Luxembourg Parkinson's study. Methods:A cross-sectional analysis comparing iPD ( = 118) and iPD ( = 378) individuals was performed, followed by the application of logistic regression models. Consequently, regression models were fitted for a subset of iPD ( = 35) vs. iPD ( = 126), utilizing a detailed neuropsychological battery to assess the association between FOG and cognitive subdomains. Both regression models were adjusted for sociodemographic confounders and disease severity. Results:iPD individuals presented with more motor complications (MDS-UPDRS IV) compared to iPD individuals. Moreover, iPD individuals exhibited a higher non-motor burden, including a higher frequency of hallucinations, higher MDS-UPDRS I scores, and more pronounced autonomic dysfunction as measured by the SCOPA-AUT. In addition, iPD individuals showed lower sleep quality along with lower quality of life (measured by PDSS and PDQ-39, respectively). The cognitive subdomain analysis in iPD vs. iPD indicated lower scores in Benton's Judgment of Line Orientation test and CERAD word recognition, reflecting higher impairment in visuospatial, executive function, and memory encoding. Conclusion:We determined a significant association between FOG and a clinical endophenotype of PD with higher non-motor burden. While our results supported the cognitive model of FOG, our findings point to a more widespread cortical impairment across cognitive subdomains beyond the executive domain in PD with additional higher impairment in visuospatial function and memory encoding.
10.3389/fnagi.2024.1479572
Randomized, Controlled Trial of Exercise on Objective and Subjective Sleep in Parkinson's Disease.
Amara Amy W,Wood Kimberly H,Joop Allen,Memon Raima A,Pilkington Jennifer,Tuggle S Craig,Reams John,Barrett Matthew J,Edwards David A,Weltman Arthur L,Hurt Christopher P,Cutter Gary,Bamman Marcas M
Movement disorders : official journal of the Movement Disorder Society
BACKGROUND:Sleep dysfunction is common and disabling in persons with Parkinson's Disease (PD). Exercise improves motor symptoms and subjective sleep quality in PD, but there are no published studies evaluating the impact of exercise on objective sleep outcomes. The goal of this study was to to determine if high-intensity exercise rehabilitation combining resistance training and body-weight interval training, compared with a sleep hygiene control improved objective sleep outcomes in PD. METHODS:Persons with PD (Hoehn & Yahr stages 2-3; aged ≥45 years, not in a regular exercise program) were randomized to exercise (supervised 3 times a week for 16 weeks; n = 27) or a sleep hygiene, no-exercise control (in-person discussion and monthly phone calls; n = 28). Participants underwent polysomnography at baseline and post-intervention. Change in sleep efficiency was the primary outcome, measured from baseline to post-intervention. Intervention effects were evaluated with general linear models with measurement of group × time interaction. As secondary outcomes, we evaluated changes in other aspects of sleep architecture and compared the effects of acute and chronic training on objective sleep outcomes. RESULTS:The exercise group showed significant improvement in sleep efficiency compared with the sleep hygiene group (group × time interaction: F = 16.0, P < 0.001, d = 1.08). Other parameters of sleep architecture also improved in exercise compared with sleep hygiene, including total sleep time, wake after sleep onset, and slow-wave sleep. Chronic but not acute exercise improved sleep efficiency compared with baseline. CONCLUSIONS:High-intensity exercise rehabilitation improves objective sleep outcomes in PD. Exercise is an effective nonpharmacological intervention to improve this disabling nonmotor symptom in PD. © 2020 International Parkinson and Movement Disorder Society.
10.1002/mds.28009