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.
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.
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.
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.
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.
1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline (Sal) is a kind of catechol isoquinoline compound, which mainly exists in mammalian brain and performs a variety of biological functions. Through in vivo metabolism, Sal can be transformed into endogenous neurotoxins and can participate the occurrence of Parkinson’s disease (PD). This has attracted widespread concern of researchers. Recently, many research works have shown that Sal may lead to alcohol addiction and regulate hormone release of the neuroendocrine system, which indicated that it is a potential regulator of dopaminergic neurons. In this paper, we discuss the neural functions of Sal on the above aspects, and wish to provide some theoretical supports for further research on its mechanism.
Evidence has been retrieved through MEDLINE and Cochrane Libray about the treatment for patients with advanced Parkinson’s disease who suffered from on-off, dyskinesia and depression after chronic use of L-dopa. All of the evidence has been evaluated. Methods of evidence-based treatment were drawn up according to the evidence, clinciams’ experiences and patients’ preferences. All symptoms of the patient have been improved obviously.
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.
Objective To investigate the association between parkin gene S/N167 polymorphism and the risk for Parkinson’s Disease (PD) using the methods of meta-analysis. Method References were retrieved through the computerized Medline, Cochrane Library and CBM search from 1998 to 2003. Similar search strategies were applied to each of these databases. The unpublished data of our study were also included.Studies eligible for this meta-analysis should meet the following inclusion criterias: ① presentation of original data and a cross-sectional design. ② PD as the outcome of interest. ③ an odds ratio (or enough information to calculate it) reported to quantify the association between the frequencies of genotypes and alleles of parkin gene S/N167 polymorphism and the risk for PD. All analyses were conducted with ’Review Manager’ Version 4.2 software. Results A total of 1 239 PD patients and 1 168 control studies were studied. The combined data statistics revealed the frequencies of the genotypes and alleles were higher, but showed no statistically difference, for the total PD group from that ofthe control group (Z=1.57, P=0.12). After stratification according to eastern or western origin, the frequencies of G/A+A/A genotype and a allele of eastern origin were significantly higher [test for overall effect: P=0.01, OR=1.41, 95%CI= (1.08 to1.83); P=0.01, OR=1.25, 95%CI= (1.08 to1.44), respectively] in the PD group than that in the control group. After including our unpublished data, the results remained constant, and the trend was much more pronounced. Conversely, there was no difference [test for overall effect: P=0.08, OR=0.55, 95%CI= (0.30 to1.02); P=0.08, OR=0.55, 95%CI= (0.28 to1.08)] in the frequencies of allele and genotype of western origin between the PD patients and the controls. Conclusions The meta-analysis suggests that the parkin gene S/N167 polymorphism might be a genetic risk factor for PD of eastern origin, but not a definite risk for PD of western origin.
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.