Recognition of Freezing of Gait in Parkinson’s Disease Based on Machine Vision

Knowledge mapping and research trends of accidental falls in patients with Parkinson's disease from 2003 to 2023: a bibliometric analysis

Background

Recent years have witnessed a rapid growth in research on accidental falls in patients with Parkinson's Disease (PD). However, a comprehensive and systematic bibliometric analysis is still lacking. This study aims to systematically analyze the current status and development trends of research related to accidental falls in patients with PD using bibliometric methods.

Methods

We retrieved literature related to accidental falls in patients with PD published between January 1, 2003, and December 31, 2023, from the Web of Science Core Collection (WoSCC) database. Statistical analysis and knowledge mapping of the literature were conducted using VOSviewer, CiteSpace, and Microsoft Excel software.

Results

A total of 3,195 publications related to accidental falls in patients with PD were retrieved. These articles were authored by 13,202 researchers from 3,834 institutions across 87 countries and published in 200 academic journals. Over the past 20 years, the number of published articles and citations has increased annually. The United States and the United Kingdom have the highest number of publications in this field, while Harvard University and Tel Aviv University are the most influential institutions. The Parkinsonism & Related Disorders journal published the highest number of articles, while the Movement Disorders journal had the highest number of citations. The most prolific author is Bloem, Bastiaan R, while the most cited author is Hausdorff, Jeffrey. The main research areas of these publications are Neurosciences, Biomedical, Electrical & Electronic, and Biochemistry & Molecular Biology. Currently, high-frequency keywords related to accidental falls in patients with PD include risk factors, clinical manifestations, and interventions. Prediction and prevention of accidental falls in such patients is a research topic with significant potential and is currently a major focus of research.

Conclusion

This study used bibliometric and knowledge mapping analysis to reveal the current research status and hotspots in the field of accidental falls in patients with PD. It also points out directions for future research. This study can provide theoretical support and practical guidance for scholars to further conduct related research.

Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center

Tremor, defined as an "involuntary, rhythmic, oscillatory movement of a body part", is a key feature of many neurological conditions including Parkinson's disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson's disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81-0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools.

Video-Based Detection of Freezing of Gait in Daily Clinical Practice in Patients With Parkinsonism

Freezing of gait (FoG) is a prevalent symptom among individuals with Parkinson's disease and related disorders. FoG detection from videos has been developed recently; however, the process requires using videos filmed within a controlled environment. We attempted to establish an automatic FoG detection method from videos taken in uncontrolled environments such as in daily clinical practices. Motion features of 16 patients were extracted from timed-up-and-go test in 109 video data points, through object tracking and three-dimension pose estimation. These motion features were utilized to form the FoG detection model, which combined rule-based and machine learning-based models. The rule-based model distinguished the frames in which the patient was walking from those when the patient has stopped, using the pelvic position coordinates; the machine learning-based model distinguished between FoG and stop using a combined one-dimensional convolutional neural network and long short-term memory (1dCNN-LSTM). The model achieved a high intraclass correlation coefficient of 0.75-0.94 with a manually-annotated duration of FoG and %FoG. This method is novel as it combines object tracking, 3D pose estimation, and expert-guided feature selection in the preprocessing and modeling phases, enabling FoG detection even from videos captured in uncontrolled environments.

Metformin improves depressive-like behavior in experimental Parkinson's disease by inducing autophagy in the substantia nigra and hippocampus

Parkinson's disease (PD) remains a disease of little known etiology. In addition to the motor symptoms, depression is present in about 40% of patients, contributing to the loss of quality of life. Recently, the involvement of the autophagy mechanism in the pathogenesis of depression has been studied, in addition to its involvement in PD as well. In this study, we tested the effects of metformin, an antidiabetic drug also with antidepressant effects, on depressive-like behavior in a rotenone-induced PD model and on the autophagy process. Mice 8-week-old male C57BL/6 were induced with rotenone for 20 consecutive days (2.5 mg/kg/day) and treated with metformin (200 mg/kg/day) from the 5th day of induction. All the animals were submitted to rotarod, sucrose preference and tail suspension tests. After euthanasia, the substantia nigra and hippocampus were removed for analysis by western blotting or fixed and analyzed by immunofluorescence. The results show that there was an impairment of autophagy in animals induced by rotenone both in nigral and extranigral regions as well as a depressive-like behavior. Metformin was able to inhibit depressive-like behavior and increase signaling pathway proteins, transcription factors and autophagosome-forming proteins, thus inducing autophagy in both the hippocampus and the substantia nigra. In conclusion, we show that metformin has an antidepressant effect in a rotenone-induced PD model, which may result, at least in part, from the induction of the autophagy process.