At present the parkinsonian rigidity assessment depends on subjective judgment of neurologists according to their experience. This study presents a parkinsonian rigidity quantification system based on the electromechanical driving device and mechanical impedance measurement method. The quantification system applies the electromechanical driving device to perform the rigidity clinical assessment tasks (flexion-extension movements) in Parkinson’s disease (PD) patients, which captures their motion and biomechanical information synchronously. Qualified rigidity features were obtained through statistical analysis method such as least-squares parameter estimation. By comparing the judgments from both the parkinsonian rigidity quantification system and neurologists, correlation analysis was performed to find the optimal quantitative feature. Clinical experiments showed that the mechanical impedance has the best correlation (Pearson correlation coefficient r = 0.872, P < 0.001) with the clinical unified Parkinson’s disease rating scale (UPDRS) rigidity score. Results confirmed that this measurement system is capable of quantifying parkinsonian rigidity with advantages of simple operation and effective assessment. In addition, the mechanical impedance can be adopted to help doctors to diagnose and monitor parkinsonian rigidity objectively and accurately.
Diagnosis of Parkinson’s disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.
Objective To assess the changes in depression symptoms in patients with Parkinson’s disease (PD) receiving combined treatment of deep brain stimulation (DBS) and antiparkinsonian drug therapy (DT) compared with under DT alone. Methods Related literature was retrieved from electronic databases, including PubMed, Cochrane Library, Embase, China National Knowledge Infrastructure, Wanfang Data, and VIP databases. Stata 14.0 software was used for statistical analysis. Network meta-analysis was performed using frequentist model to compare different interventions with each other. Results Five cohort studies and seven randomized controlled trials (RCTs) were included. The total number of participants was 1241. Assessed by the Beck Depression Inventory (BDI) score as the primary outcome, patients who received DT alone showed worse outcome in depression as compared to those who received subthalamic nucleus (STN)-DBS plus DT [standardized mean difference (SMD)=0.30, 95% confidence interval (CI) (0.01, 0.59), P<0.05], and there was no significant difference between the patients receiving globus pallidus interna (GPi)-DBS plus DT and those receiving STN-DBS plus DT [SMD=–0.12, 95%CI (–0.41, 0.16), P>0.05] or those receiving DT alone [SMD=–0.42, 95%CI (–0.84, 0.00), P>0.05]. Assessed by BDI-Ⅱ as the primary outcome, patients who received DT alone showed worse outcome in depression than those who received STN-DBS plus DT [SMD=0.29, 95%CI (0.05, 0.54), P<0.05]; compared with STN-DBS plus DT and DT alone, GPi-DBS plus DT was associated with better improvement in depression [SMD=–0.26, 95%CI (–0.46, –0.06), P<0.05; SMD=–0.55, 95%CI (–0.88, –0.23), P<0.05]. The ranking results of surface under the cumulative ranking curves showed that DBS plus DT had a better superiority in depression symptoms, and GPi-DBS was better than STN-DBS. Conclusion Compared with DT, STN-DBS plus DT is more likely to improve the depressive symptoms of PD patients, and GPi-DBS may be better than STN-DBS.
Methods for achieving diagnosis of Parkinson’s disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classification accuracy of the proposed method is significantly improved on two public datasets and the increasement of mean accuracy is up to 25.44%. This method not only effectively improves the classification accuracy of PD speech dataset, but also increases the sample utilization rate, providing a new idea for the diagnosis of PD.
The application of dopamine agonists in Parkinson’s disease has been a hot topic in recent years. Can dopamine receptor agonists serve as the initial drugs for Parkinson’s disease? Does it improve the natural history of patients? Has it neuroprotective role? When and how to use dopamine receptor agonists? This article provides evidence on the pros and cons of dopamine receptor agonists in the treatment of Parkinson’s disease for helping clinical decision making.
Objective To understand the frailty status and main influencing factors of elderly Parkinson’s disease (PD) patients. Methods The elderly PD patients who attended the Department of Neurology of Changshu Hospital of Traditional Chinese Medicine between November 2023 and March 2024 were selected. The patients’ frailty conditions were investigated using general information questionnaire, Chinese version of Tilburg Frailty Indicator, Hoehn-Yahr Rating Scale, Mini-Nutritional Assessment Short Form, Movement Disorder Society-Unified PD Rating Scale Part Ⅲ, PD Sleep Scale-2, and Mini-Mental State Examination. Multiple linear regression analysis was used to further determine the influencing factors of the frailty status in elderly PD patients. Results A total of 170 PD patients were included. Among them, 117 cases (68.82%) had frailty, while 53 cases (31.18%) had not frailty. The average score for frailty was (6.48±3.34) points, the average score for nutritional status was (11.89±1.65) points, the average score for motor function was (27.40±13.73) points, the average score for sleep quality was (16.05±7.76) points, and the average score for cognitive status is (26.25±4.51) points. The Pearson correlation analysis results showed that PD patient frailty was positively correlated with motor function and sleep quality (P<0.01), and negatively correlated with nutritional status and cognitive status (P<0.01). The results of multiple linear regression analysis showed that age, education, place of residence, course of disease, Hoehn-Yahr Rating, nutritional status, motor function, cognitive status and sleep quality were the influencing factors of frailty in PD patients (P<0.05). Conclusions Elderly PD patients are prone to frailty. Healthcare professionals should pay attention to early screening for frailty in this population and provide timely and effective interventions to prevent or delay the onset of frailty in patients.
Parkinson’s disease is a common neurodegenerative disorder with continuously rising incidence rates. Existing pharmacological treatments have complications and cannot halt disease progression. Transcranial focused ultrasound stimulation (tFUS), as a novel neuromodulation technology, demonstrates unique advantages in Parkinson’s disease treatment. tFUS exerts multiple effects through mechanical mechanisms at multiple levels, including protecting dopaminergic neurons, regulating neurotransmitter systems, and improving neural circuit function. Preclinical studies have confirmed its potential in improving both motor and non-motor symptoms, and early clinical studies have shown good safety profiles. However, the clinical translation of tFUS still faces challenges such as parameter optimization and individualized treatment protocols, requiring validation of long-term efficacy through large-scale clinical trials.
ObjectiveTo summarize and evaluate the quality of methodology, report and evidence of the systematic reviews and meta-analyses (SRs/MAs) of acupuncture and moxibustion interventions for Parkinson's disease. MethodsEight databases including CNKI, WanFang Data, VIP, CBM, PubMed, EMbase, Cochrane Library and Web of Science were searched from inception to May 1, 2023. The quality of methodology, report and evidence involved in these studies were evaluated by AMSTAR 2, PRISMA and GRADE tool. ResultsA total of 28 SRs/MAs were included, and the findings of included studies showed that acupuncture and moxibustion had a clinical advantage for Parkinson's disease. The methodological quality of all studies was extremely low. Thirteen reports were relatively complete, 14 reports had certain flaws, and 1 report had relatively serious flaws. And of the 126 reports for seven outcomes, 1 was graded as high, 12 as moderate, 57 as low, and 56 as critically low. ConclusionThe current evidence shows that acupuncture and moxibustion have a certain clinical effect for Parkinson's disease, but the methodological quality and evidence quality of related SRs/MAs are low, and the standardization still needs to be improved. The efficacy of acupuncture and moxibustion in Parkinson's disease still needs to be verified by high-quality clinical studies in the future.
For speech detection in Parkinson’s patients, we proposed a method based on time-frequency domain gradient statistics to analyze speech disorders of Parkinson’s patients. In this method, speech signal was first converted to time-frequency domain (time-frequency representation). In the process, the speech signal was divided into frames. Through calculation, each frame was Fourier transformed to obtain the energy spectrum, which was mapped to the image space for visualization. Secondly, deviations values of each energy data on time axis and frequency axis was counted. According to deviations values, the gradient statistical features were used to show the abrupt changes of energy value in different time-domains and frequency-domains. Finally, KNN classifier was applied to classify the extracted gradient statistical features. In this paper, experiments on different speech datasets of Parkinson’s patients showed that the gradient statistical features extracted in this paper had stronger clustering in classification. Compared with the classification results based on traditional features and deep learning features, the gradient statistical features extracted in this paper were better in classification accuracy, specificity and sensitivity. The experimental results show that the gradient statistical features proposed in this paper are feasible in speech classification diagnosis of Parkinson’s patients.
At present, the incidence of Parkinson’s disease (PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram (EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band (P = 0.034) and State5 of Gamma frequency band (P = 0.010) could be used to differentiate health controls and off-medication Parkinson’s disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson’s disease.
