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.
Parkinson’s disease is a common chronic progressive neurodegenerative disease, and its main pathological change is the degeneration and loss of dopaminergic neurons in substantia nigra striatum. Vitamin D receptors are widely distributed in neurons and glial cells, and the normal function of substantia nigra striatum system depends on the level of vitamin D and the normal expression of vitamin D receptors. In recent years, from basic to clinical research, there are some differences in the conclusion of the correlation of vitamin D and its receptor gene polymorphism with Parkinson’s disease. This paper aims to review the research on the correlation of vitamin D and vitamin D receptor gene polymorphism with Parkinson’s disease, and discuss the future research direction in this field.
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.
Dysphagia is a common non-motor symptom in Parkinson’s disease (PD), with a high incidence and insidious progression. It can lead to complications such as dehydration, malnutrition, aspiration pneumonia, and even death, seriously affecting the quality of life and prognosis of patients. Therefore, early screening, assessment, and intervention are crucial for improving the quality of life and prognosis of PD patients with dysphagia. This article mainly reviews the risk factors and management strategies of dysphagia in PD, with the aim of providing a reference for healthcare professionals to conduct subsequent evaluations and develop targeted interventions.
People with Parkinson’s disease (PD) exhibit multi-system damaged. Medication mainly targets impairments related to dopaminergic lesions. Moreover, in later stages of the disease, medication becomes less effective. Rehabilitation therapy is believed that it can improve multiple functional disorders, including myotonia, bradykinesia, and postural gait abnormalities. It not only reduces the severity of non-motor symptoms and improves the quality of life in PD patients, but also delays the development of PD and improves the activity of daily life of patients. This article summarizes the progress of rehabilitation assessment and the therapy of PD.
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 evaluate the effectiveness of repetitive transcranial magnetic stimulation (rTMS) for treating dysfunction in patients with Parkinson’s disease (PD). Methods We searched the Cochrane Library (Issue 1, 2010), MEDLINE, EMbase, CBMdisc, and CNKI from the date of the database establishment to April 2010. Randomized controlled trials (RCTs) of rTMS for patients with PD were collected. The quality of the included RCTs was critically appraised and data were extracted by two reviewers independently. Meta-analyses were conducted for the eligible RCTs. Results Eight RCTs were included. The pooled results of the first 2 RCTs showed that, there was no significant difference compared with control group about treating PD patients with clinical motor dysfunction by high-frequency rTMS 10 days later (WMD= –4.75, 95%CI –13.73 to 4.23). The pooled analysis of another 3 studies showed that, no significant difference were found about improving symptoms with treatment of low-frequency rTMS for 1 month compared with control group (WDM= –8.51, 95%CI –18.48 to 1.46). The pooled analysis of last 3 studies showed that, patient with treatment of low-frequency rTMS for 3 months, had been significantly improved in clinical symptoms such as neurological, behavior and emotional state, clinical motor function, and activities of daily living (WDM= –5.79, 95%CI –8.44 to –1.13). The frontal or motor cortex rTMS manifested as low frequency (≤1Hz), high intensity (≥90% RMT), multi-frequency (≥3 times) and long time (≥3 months) had a positive effect on the clinical symptoms of patients with PD and also had a long-term effect. Conclusions rTMS can improve clinical symptoms and dysfunction of the patients with PD.
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.
Objective To review the progress of perioperative treatments for patients of Parkinson’s disease and hip fractures. Methods The related literature of treatments for patients of Parkinson’s disease and hip fractures were reviewed and analyzed from the aspects such as the perioperative management, selection of operation ways, and prognosis. Results The patients of Parkinson’s disease are more likely to sustain hip fractures because of postural instability and osteoporosis. The perioperative treatments for patients of Parkinson’s disease and hip fractures should be determined by orthopedists, neurologist, anesthesiologist, and physical therapist. There is still controversy about the selection of operation and surgical approach. And the prognosis of patients of Parkinson’s disease and hip fractures are associated with the severity of Parkinson’s disease. Conclusion There are few clinical studies about the patients of Parkinson’s disease and hip fractures. The mid-term and long-term functional outcomes of patients of Parkinson’s disease and hip fractures are unsufficient. And the best treatments of patients of Parkinson’s disease and hip fractures need to be further explored.
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.