Machine learning models for diagnosis of essential tremor and dystonic tremor using grey matter morphological networks.

BACKGROUND: Essential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain grey matter (GM) morphological networks and combine those with machine learning models. METHODS: 3D-T1 structural images of 75 ET patients, 71 DT patients, and 79 healthy controls (HCs) were acquired. We used voxel-based morphometry to obtain GM images and constructed GM morphological networks based on the Kullback-Leibler divergence-based similarity (KLS) method. We used the GM volumes, morphological relations, and global topological properties of GM-KLS morphological networks as input features. We employed three classifiers to perform the classification tasks. Moreover, we conducted correlation analysis between discriminative features and clinical characteristics. RESULTS: 16 morphological relations features and 1 global topological metric were identified as the discriminative features, and mainly involved the cerebello-thalamo-cortical circuits and the basal ganglia area. The Random Forest (RF) classifier achieved the best classification performance in the three-classification task, achieving a mean accuracy (mACC) of 78.7%, and was subsequently used for binary classification tasks. Specifically, the RF classifier demonstrated strong classification performance in distinguishing ET vs. HCs, ET vs. DT, and DT vs. HCs, with mACCs of 83.0 %, 95.2 %, and 89.3 %, respectively. Correlation analysis demonstrated that four discriminative features were significantly associated with the clinical characteristics. CONCLUSION: This study offers new insights into the structural network mechanisms of ET and DT. It demonstrates the effectiveness of combining GM-KLS morphological networks with machine learning models in distinguishing between ET, DT, and HCs.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

Assessment of bidirectional relationships between multiple sclerosis and epilepsy: A two-sample Mendelian randomization study.

BACKGROUND AND OBJECTIVE: Epidemiological studies indicate that multiple sclerosis (MS) is associated with epilepsy. However, the causality and directionality of this association remain under-elucidated. This study aimed to reveal the causality between MS and epilepsy. METHODS: A two-sample Mendelian randomization (MR) analysis was performed by using summarized statistics derived from large genome-wide association studies of MS and epilepsy. We used the inverse variance weighted method as the primary approach, and then four other MR methods to bidirectionally evaluate the causality of the association between MS and epilepsy. Additional sensitivity analyses were performed to measure the robustness of the findings. RESULTS: Genetically predicted MS was positively correlated with developing all epilepsy [odds ratio (OR) = 1.027 (1.003-1.051), P  =  0.028] and generalized epilepsy [OR = 1.050 (1.008-1.094), P = 0.019]. In the reverse MR analysis, all epilepsy [OR = 1.310 (1.112-1.543), P = 0.001], generalized epilepsy [OR = 1.173 (1.010-1.363), P = 0.037], and focal epilepsy [OR = 1.264 (1.069-1.494), P  =  0.006] elevated the risk of developing MS. The result remained robust and congruous across all sensitivity analyses conducted. CONCLUSIONS: MS is potentially associated with a higher risk of developing epilepsy. Furthermore, epilepsy may be a causal determinant of MS risk. These findings may further the understanding of the interaction of the two conditions.

Altered connectivity in the cognitive control-related prefrontal cortex in Parkinson's disease with rapid eye movement sleep behavior disorder.

Rapid eye movement sleep behavior disorder (RBD) frequently occurs in Parkinson's disease (PD), however, the exact pathophysiological mechanism is not clear. The prefrontal cortex (PFC), especially ventrolateral prefrontal cortex (VLPFC), dorsolateral prefrontal cortex (DLPFC), and inferior frontal gyrus (IFG) which may play roles by regulating cognitive control processes. The purpose of this study was to investigate whether there is abnormal functional connectivity (FC) maps and volume changes in PD with RBD(PD-RBD). We recruited 20 PD-RBD, 20 PD without RBD (PD-nRBD), and 20 normal controls (NC). We utilized resting-state functional Magnetic Resonance Imaging (rs-MRI) to explore FC changes based on regions of interest (VLPFC, DLPFC, and IFG), and used voxel-based morphology technology to analyze whole-brain volumes by 3D-T1 structural MRI. Except the REM sleep behavioral disorders questionnaire (RBDSQ), the PD-RBD showed lower visuospatial/executive and attention scores than the NC group. The RBDSQ scores were significantly positively correlated with zFC of right DLPFC to bilateral posterior cingulate cortex (PCC) (P = 0.0362, R = 0.4708, AlphaSim corrected) and also significantly positively correlated with zFC of left VLPFC to right inferior temporal (P = 0.0157, R = 0.5323, AlphaSim corrected) in PD-RBD group. Furthermore, abnormal correlations with zFC values were also found in some cognitive subdomains in PD-RBD group. The study may suggest that in PD-RBD patients, the presence of RBD may be related to the abnormal FC of VLPFC and DLPFC, meanwhile, the abnormal FC of DLPFC and IFG may be related to the mechanisms of cognitive impairment.

Antihypertensive drugs may not delay the symptom progression of Parkinson's disease: A 2-year follow-up study.

Background: Parkinson's disease (PD) is one of the most common neurodegenerative disease, and half of PD patients have hypertension as well. The effect of antihypertensive drugs on the progression of PD has been less studied. The focus of this study was on the changes in dopamine transporter (DAT) levels to assess the effect of antihypertensive drugs on the progression of PD. Methods: Data from 321 drug-naïve patients from the Parkinson's Disease Progression Marker Initiative (PPMI) were collected over a 2-year period. Patients were divided into the PD with arterial hypertension (AH) group (102 cases) with antihypertensive drugs, the PD with other cardiovascular risk factors (CVRFs) group (60 cases) with antidiabetic and/or lipid-lowering drugs, and the pure PD group (159 cases) without CVRFs. The Movement Disorder Society Sponsored Revision Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Hoehn-Yahr (H&Y) stage were used to assess progression. DAT semiquantitative values were used to evaluate damage to dopaminergic neurons in the substantia nigra, including the contralateral and ipsilateral count density ratio and asymmetry index. Results: There were no significant differences among the three groups in MDS-UPDRS score and H&Y stage. Changes in DAT levels among the three groups were without distinct differences in the first year and second year. In each group, DAT decreased more in the first year than in the second year. There was no decrease in DAT uptake in the PD with AH group compared with the other groups during the follow-up period. Conclusions: There is no evidence that antihypertensive drugs can delay PD progression within 2 years.

Cerebrospinal fluid GFAP is a predictive biomarker for conversion to dementia and Alzheimer's disease-associated biomarkers alterations among de novo Parkinson's disease patients: a prospective cohort study.

BACKGROUND: Dementia is a prevalent non-motor manifestation among individuals with advanced Parkinson's disease (PD). Glial fibrillary acidic protein (GFAP) is an inflammatory marker derived from astrocytes. Research has demonstrated the potential of plasma GFAP to forecast the progression to dementia in PD patients with mild cognitive impairment (PD-MCI). However, the predictive role of cerebrospinal fluid (CSF) GFAP on future cognitive transformation and alterations in Alzheimer's disease (AD)-associated CSF biomarkers in newly diagnosed PD patients has not been investigated. METHODS: 210 de novo PD patients from the Parkinson's Progression Markers Initiative were recruited. Cognitive progression in PD participants was evaluated using Cox regression. Cross-sectional and longitudinal associations between baseline CSF GFAP and cognitive function and AD-related CSF biomarkers were evaluated using multiple linear regression and generalized linear mixed model. RESULTS: At baseline, the mean age of PD participants was 60.85 ± 9.78 years, including 142 patients with normal cognition (PD-NC) and 68 PD-MCI patients. The average follow-up time was 6.42 ± 1.69 years. A positive correlation was observed between baseline CSF GFAP and age (ß = 0.918, p < 0.001). There was no statistically significant difference in baseline CSF GFAP levels between PD-NC and PD-MCI groups. Higher baseline CSF GFAP predicted greater global cognitive decline over time in early PD patients (Montreal Cognitive Assessment, ß = - 0.013, p = 0.014). Furthermore, Cox regression showed that high baseline CSF GFAP levels were associated with a high risk of developing dementia over an 8-year period in the PD-NC group (adjusted HR = 3.070, 95% CI 1.119-8.418, p = 0.029). In addition, the baseline CSF GFAP was positively correlated with the longitudinal changes of not only CSF α-synuclein (ß = 0.313, p < 0.001), but also CSF biomarkers associated with AD, namely, amyloid-ß 42 (ß = 0.147, p = 0.034), total tau (ß = 0.337, p < 0.001) and phosphorylated tau (ß = 0.408, p < 0.001). CONCLUSIONS: CSF GFAP may be a valuable prognostic tool that can predict the severity and progression of cognitive deterioration, accompanied with longitudinal changes in AD-associated pathological markers in early PD.

Evaluation of Nocturnal Symptoms in Chinese Parkinson's Disease Patients Based on the PDSS-2 Scale: A Multicenter Cross-Sectional Study.

BACKGROUND: Nocturnal symptoms have a significant effect on the quality of life in Parkinson's disease (PD) patients. OBJECTIVE: This study aimed to investigate the prevalence and associated factors of nocturnal symptoms in Chinese PD patients. METHODS: This multicenter cross-sectional study included 1,500 patients with primary PD from 18 centers in China was carried out between February 2019 and February 2020. Questionnaires including Parkinson's disease sleep scale 2 (PDSS-2), Parkinson's disease questionnaire 8 (PDQ-8), Beck depression inventory (BDI), and generalized anxiety disorder scale 7 (GAD-7) were used to assess nocturnal symptoms, quality of life, depression, and anxiety. RESULTS: Among 1,500 Chinese PD patients, 576 (38.4%) reported nocturnal symptoms. Of them, 59.2% were older than 65 years. The PDQ-8 total score was higher in patients with nocturnal symptoms (p < 0.01). Moderate and severe depression was reported more often in patients with nocturnal symptoms (p < 0.01), and the occurrence and severity of anxiety were higher as well (p < 0.01). Longer disease duration and higher Hoehn-Yahr (HY) stage were independently associated with nocturnal symptoms (p < 0.01). Education level, depression, disease course, HY stage, and nocturnal symptoms were related to the quality of life in Chinese PD patients (p < 0.01). CONCLUSION: Our study found that 38.4% of Chinese PD patients have nocturnal symptoms, even in early and mid-stage PD. Nocturnal symptoms were associated with worse quality of life and higher incidences of depression and anxiety. Nocturnal symptoms should be included in the assessment and care plan, especially in patients with longer disease courses and higher HY stages.

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor.

Background: Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients. Methods: The histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics. Results: Each classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity. Conclusion: Our findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients.

White matter hyperintensities burden in the frontal regions is positively correlated to the freezing of gait in Parkinson's disease.

Objective: Previous studies have reported that white matter hyperintensities (WMHs) are associated with freezing of gait (FOG), but it is not clear whether their distribution areas have correlations with FOG in Parkinson's disease (PD) and the potential influencing factors about WMHs. Methods: Two hundred and forty-six patients with PD who underwent brain MRI were included. Participants were divided into PD with FOG (n = 111) and PD without FOG (n = 135) groups. Scheltens score was used to assess the WMHs burden in the areas of deep white matter hyperintensities (DWMHs), periventricular hyperintensities (PVHs), basal ganglia hyperintensities (BGHs), and infratentorial foci of hyperintensities (ITF). Whole brain WMHs volume was evaluated by automatic segmentation. Binary logistic regression was used to evaluate relationships between WMHs and FOG. The common cerebrovascular risk factors that may affect WMHs were evaluated by mediation analysis. Results: There were no statistical differences between PD with and without FOG groups in whole brain WMHs volume, total Scheltens score, BGHs, and ITF. Binary logistic regression showed that the total scores of DWMHs (OR = 1.094; 95% CI, 1.001, 1.195; p = 0.047), sum scores of PVHs and DWMHs (OR = 1.080; 95% CI, 1.003, 1.164; p = 0.042), especially the DWMHs in frontal (OR = 1.263; 95% CI, 1.060, 1.505 p = 0.009), and PVHs in frontal caps (OR = 2.699; 95% CI, 1.337, 5.450; p = 0.006) were associated with FOG. Age, hypertension, and serum alkaline phosphatase (ALP) are positively correlated with scores of DWMHs in frontal and PVHs in frontal caps. Conclusion: These results indicate that WMHs distribution areas especially in the frontal of DWMHs and PVHs play a role in PD patients with FOG.

Identification of essential tremor based on resting-state functional connectivity.

Currently, machine-learning algorithms have been considered the most promising approach to reach a clinical diagnosis at the individual level. This study aimed to investigate whether the whole-brain resting-state functional connectivity (RSFC) metrics combined with machine-learning algorithms could be used to identify essential tremor (ET) patients from healthy controls (HCs) and further revealed ET-related brain network pathogenesis to establish the potential diagnostic biomarkers. The RSFC metrics obtained from 127 ET patients and 120 HCs were used as input features, then the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) methods were applied to reduce feature dimensionality. Four machine-learning algorithms were adopted to identify ET from HCs. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performances. The support vector machine, gradient boosting decision tree, random forest and Gaussian naïve Bayes algorithms could achieve good classification performances with accuracy at 82.8%, 79.4%, 78.9% and 72.4%, respectively. The most discriminative features were primarily located in the cerebello-thalamo-motor and non-motor circuits. Correlation analysis showed that two RSFC features were positively correlated with tremor frequency and four RSFC features were negatively correlated with tremor severity. The present study demonstrated that combining the RSFC matrices with multiple machine-learning algorithms could not only achieve high classification accuracy for discriminating ET patients from HCs but also help us to reveal the potential brain network pathogenesis in ET.

Combined brain network topological metrics with machine learning algorithms to identify essential tremor.

Background and objective: Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics. Results: All classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity. Conclusion: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.

Dexmedetomidine alleviates pain in MPTP-treated mice by activating the AMPK/mTOR/NF-κB pathways in astrocytes.

Pain is a major non-motor symptom that contributes to impaired quality of life in Parkinson's disease (PD). However, the mechanisms and treatment of pain in PD have not been well studied. Dexmedetomidine (Dex) is used for analgesia and sedation during deep brain stimulation (DBS) and may reverse the progression of PD. Here, we explored the effect of Dex on Parkinson's pain and the underlying mechanism. C57BL/6 mice were intraperitoneally injected with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP, 30 mg/kg) to establish a PD model. Then, the mice were treated with Dex (50 µg/kg) or Compound C (CC, 10 mg/kg, AMPK inhibitor). A motor behavioral test was used to validate the PD model, and a plantar test was conducted to assess mechanical and thermal stimulation thresholds. Immunofluorescence and western blotting were used to analyze the level of tyrosine hydroxylase (TH) in the substantia nigra (SN) and the expression of c-Fos, GFAP, p-AMPK, mTOR, NF-κB, TNFα, and IL-6 in the dorsal horn of the spinal cord (DHSC). We found that mice exhibited motor dysfunction and mechanical allodynia and thermal hyperalgesia after MPTP injection, and these changes were partially reversed by Dex. Dex also reduced MPTP-induced astrocyte activation and TNFα and IL-6 expression, increased p-AMPK and reduced mTOR and NF-κB expression in DHSC. Moreover, the effects of Dex were partially reversed by the AMPK inhibitor Compound C. Conclusions: These findings reveal that Dex protects dopaminergic neurons in PD and alleviates pain by reducing the activation of DHSC astrocytes through the AMPK/mTOR/NF-κB pathway. Therefore, Dex may be a potential drug for treating Parkinson's pain.

Dexmedetomidine Can Enhance PINK1/Parkin-Mediated Mitophagy in MPTP-Induced PD Mice Model by Activating AMPK.

Parkinson's disease (PD) is a common neurodegenerative disease characterized by the degeneration of dopaminergic (DA) neurons in the substantia nigra (SN). Our previous study has shown that dexmedetomidine (Dex) can protect mitochondrial function and reduce apoptosis in MPP+-induced SH-SY5Y cells. Evidences have shown that mitophagy is related to the development of PD. In this study, we investigated whether Dex can enhance mitophagy in MPTP-induced mice to play a neuroprotective effect. In our experiment, mice were injected with MPTP 30 mg/kg intraperitoneally for 5 consecutive days to establish a PD subacute model. Dex (30, 50, and 100 µg/kg) was injected intraperitoneally 30 minutes before each injection of MPTP, respectively. Our results showed that Dex (50 µg/kg) most significantly attenuated MPTP-induced motor dysfunction and restored TH-positive neurons in the SN, increased the expression of the antiapoptotic protein Bcl-2, and decreased the expression of apoptotic proteins cleaved casepase3, cleaved casepase9, and Bax. Moreover, Dex increased the activity of mitochondrial Complexes I-IV and decreased the level of oxidative stress, manifesting as decreased MDA levels and increased SOD and GSH-PX levels. Besides, under transmission electron microscopy, Dex increased the mitophagosome which is an autophagosome with a mitochondrion-like structure inside under the electron microscope. In addition, Dex could also increase the expression of mitophagy-related proteins p-AMPK, LC3II/I, PINK1, and Parkin and decrease P62. However, after using Compound C (CC, 10 mg/kg, AMPK inhibitor), the effects of Dex on increasing PINK1/Parkin-induced mitophagy and neuroprotection were attenuated. In conclusion, Dex may improve mitochondrial function by activating AMPK to enhance PINK1/Parkin-induced mitophagy, thereby protecting dopaminergic neurons.

The Chinese Parkinson's Disease Registry (CPDR): Study Design and Baseline Patient Characteristics.

BACKGROUND: There is a lack of large multicenter Parkinson's disease (PD) cohort studies and limited data on the natural history of PD in China. OBJECTIVES: The objective of this study was to launch the Chinese Parkinson's Disease Registry (CPDR) and to report its protocol, cross-sectional baseline data, and prospects for a comprehensive observational, longitudinal, multicenter study. METHODS: The CPDR recruited PD patients from 19 clinical sites across China between January 2018 and December 2020. Clinical data were collected prospectively using at least 17 core assessment scales. Patients were followed up for clinical outcomes through face-to-face interviews biennially. RESULTS: We launched the CPDR in China based on the Parkinson's Disease & Movement Disorders Multicenter Database and Collaborative Network (PD-MDCNC). A total of 3148 PD patients were enrolled comprising 1623 men (51.6%) and 1525 women (48.4%). The proportions of early-onset PD (EOPD, age at onset ≤50 years) and late-onset PD (LOPD) were 897 (28.5%) and 2251 (71.5%), respectively. Stratification by age at onset showed that EOPD manifested milder motor and nonmotor phenotypes and was related to increased probability of dyskinesia. Comparison across genders suggested a slightly older average age at PD onset, milder motor symptoms, and a higher rate of developing levodopa-induced dyskinesias in women. CONCLUSIONS: The CPDR is one of the largest multicenter, observational, longitudinal, and natural history studies of PD in China. It offers an opportunity to expand the understanding of clinical features, genetic, imaging, and biological markers of PD progression. © 2022 International Parkinson and Movement Disorder Society.

Parkinson's disease patients with freezing of gait have more severe voice impairment than non-freezers during "ON state".

BACKGROUND: Speech disorders and freezing of gait (FOG) in Parkinson's disease (PD) may have some common pathological mechanisms. The purpose of this study was to compare the acoustic parameters of PD patients with dopamine-responsive FOG (PD-FOG) and without FOG (PD-nFOG) during "ON state" and explore the ability of "ON state" voice features in distinguishing PD-FOG from PD-nFOG. METHODS: A total of 120 subjects, including 40 PD patients with dopamine-responsive FOG, 40 PD-nFOG, and 40 healthy controls (HCs) were recruited. All subjects underwent neuropsychological tests. Speech samples were recorded through the sustained vowel pronunciation tasks during the "ON state" and then analyzed by the Praat software. A set of 27 voice features was extracted from each sample for comparison. Support vector machine (SVM) was used to build mathematical models to classify PD-FOG and PD-nFOG. RESULTS: Compared with PD-nFOG, the jitter, the standard deviation of fundamental frequency (F0SD), the standard deviation of pulse period (pulse period SD) and the noise-homophonic-ratio (NHR) were increased, and the maximum phonation time (MPT) was decreased in PD-FOG. The above voice features were correlated with the freezing of gait questionnaire (FOGQ). The average accuracy, specificity, and sensitivity of SVM models based on 27 voice features for classifying PD-FOG and PD-nFOG were 73.57%, 75.71%, and 71.43%, respectively. CONCLUSIONS: PD-FOG have more severe voice impairment than PD-nFOG during "ON state".

Abnormal connectivity model of raphe nuclei with sensory-associated cortex in Parkinson's disease with chronic pain.

BACKGROUND AND OBJECTIVE: There are indicates that raphe nuclei may be involved in the occurrence of chronic pain in Parkinson's disease (PD). In the study, we investigated the functional connectivity pattern of raphe nuclei in Parkinson's disease with chronic pain (PDP) to uncover its possible pathophysiology. METHODS: Fifteen PDP, who suffered from pain, lasted longer than 3 months, sixteen Parkinson's disease patients with no pain (nPDP) and eighteen matched normal health controls (NCs) were recruited. All subjects completed the King's Parkinson's Pain Scale (KPPS) besides Parkinson-related scale and demographics. We performed a seed-based resting-state analysis of functional magnetic resonance imaging to explore whole-brain functional connectivity of the raphe nuclei. Multiple regression model was used to explore the related factors of pain including disease duration, disease severity, Hamilton Depression Rating Scale, age, sex, levodopa equivalent dose and the strength of network functional connectivity. RESULTS: Compared with the nPDP, the PDP group showed stronger functional connectivity between raphe nuclei and pain-related brain regions, including parietal lobe, insular lobe, cingulum cortex and prefrontal cortex, and the functional connectivity values of those areas were significantly positively correlated with KPPS independent of the clinical variables. Compared with NCs, the combined PD groups showed decreased functional connectivity including prefrontal cortex and cingulum cortex. CONCLUSIONS: Abnormal functional connectivity model of raphe nuclei may be partly involved in pathophysiological mechanism of pain in PD.

Disorganized resting-state functional connectivity between the dorsal attention network and intrinsic networks in Parkinson's disease with freezing of gait.

Freezing of gait (FOG) is a common and complex manifestation of Parkinson's disease (PD) and is associated with impairment of attention. The purpose of this study was to evaluate the functional network connectivity (FNc) changes between the dorsal attention network (DAN) and the other seven intrinsic networks relevant to attention, visual-spatial, executive and motor functions in PD with or without FOG. Forty-three idiopathic PD patients (21 with FOG [FOG+] versus 22 without FOG [FOG-]) and 18 healthy controls (HC) were recruited in this study. The data-driven independent component analysis (ICA) method was used to extract and analyze the above-mentioned resting-state networks (RSNs). Compared with FOG-, FOG+ displayed decreased positive connectivity between the DAN and medial visual network (mVN) and sensory-motor network (SMN) and increased negative connectivity between the DAN and default mode network (DMN). The within-network connectivity in the SMN and visual networks were decreased, whereas the connectivity within DMN was increased significantly in FOG+. Correlation analysis showed that the clock drawing test (CDT) scores were positively correlated with the functional connectivity of mVN (r = 0.573, p = 0.008) and lateral visual network (lVN) (r = 0.510, p = 0.022), the Timed Up and Go Test (TUG) duration were negatively correlated with the connectivity of SMN (r = -0.629, p = 0.003), and the Frontal Assessment Battery (FAB) scores were negatively correlated with the connectivity of DMN in FOG+. Functional connectivity was changed in multiple intra-networks in patients with FOG. Inordinate inter-network connectivity between the DAN and other intrinsic networks may partly contribute to the mechanism of freezing.

Gastrodin promotes hippocampal neurogenesis via PDE9-cGMP-PKG pathway in mice following cerebral ischemia.

Gastrodin, which is extracted from the Chinese herbal medicine Gastrodia elata Blume, can ameliorate neurogenesis after cerebral ischemia. However, it's possible underlying mechanisms remain still elusive. PDE9-cGMP-PKG signaling pathway is involved in the proliferation of neural stem cells (NSCs) after cerebral ischemia. In this study, we investigated whether the beneficial effect of gastrodin on hippocampal neurogenesis after cerebral ischemia is correlated with the PDE9-cGMP-PKG signaling pathway. Bilateral common carotid artery occlusion (BCCAO) in mice and oxygen-glucose deprivation/reoxygenation (OGD/R) in primary cultured hippocampal NSCs were used to mimic brain ischemic injury. The Morris water maze (MWM) test was executed to detect spatial learning and memory. Proliferation, differentiation, and mature neurons were examined using immunofluorescence. The survival and proliferation of NSCs were assessed by CCK-8 assay and BrdU immunofluorescence staining, respectively. ELISA and western blot were used to detect the level of the PDE9-cGMP-PKG signaling pathway. In BCCAO mice, administering gastrodin (50 and 100 mg/kg) for 14 d restored cognitive behaviors; meanwhile, neurogenesis in hippocampus was stimulated, and PDE9 was inhibited and cGMP-PKG was activated by gastrodin. Consistent with the results, administering gastrodin (from 0.01-1 µmol/L) for 48 h dose-dependently ameliorated the cell viability and promoted greatly the proliferation in primary hippocampal NSCs exposed to OGD/R. Gastrodin further decreased PDE9 activity and up-regulated cGMP-PKG level. KT5823, a PKG inhibitor, markedly abrogated the protective effects of gastrodin on OGD/R-injured NSCs, accompanied by the down-regulation of PKG protein expression, but had no effects on PDE9 activity and cGMP level. Gastrodin could accelerate hippocampal neurogenesis after cerebral ischemia, which is mediated, at least partly, by PDE9-cGMP-PKG signaling pathway.

Effect of TO901317 on GF to promote the differentiation of human bone marrow mesenchymal stem cells into dopamine neurons on Parkinson's disease.

BACKGROUND: Human bone marrow mesenchymal stem cells (hBMSCs) could differentiate into dopamine-producing cells and ameliorate behavioral deficits in Parkinson's disease (PD) models. Liver X receptors (LXRs) are involved in the maintenance of the normal function of central nervous system myelin. Therefore, the previous work of our team has found the induction of cocktail-induced to dopaminergic (DA) phenotypes from adult rat BMSCs by using sonic hedgehog (SHH), fibroblast growth factor 8 (FGF8), basic fibroblast growth factor (bFGF), and TO901317 (an agonist of LXRs) with 87.42% of efficiency in a 6-day induction period. But we did not verify whether the induced cells had the corresponding neural function. METHODS: Expressions of LXRα, LXRß, and tyrosine hydroxylase (TH) were detected by immunofluorescence and western blot. Adenosine triphosphate-binding cassette transporter A1 (ABCA1) was detected by quantitative real-time PCR. The induced cells were transplanted into PD rats to study whether the induced cells are working. RESULTS: The induced cells can release the dopamine transmitter; the maximum induction efficiency of differentiation of hBMSCs into DA neurons was 91.67% under conditions of combined use with TO901317 and growth factors (GF). When the induced-cells were transplanted into PD rats, the expression of TH in the striatum increased significantly, and the behavior of PD rats induced by apomorphine was significantly improved. CONCLUSION: The induced cells have the function of DA neurons and have the potential to treat PD. TO901317 promoted differentiation of hBMSCs into DA neurons, which may be related to activation of the LXR-ABCA1 signaling pathway.

Altered regional homogeneity and connectivity in cerebellum and visual-motor relevant cortex in Parkinson's disease with rapid eye movement sleep behavior disorder.

OBJECTIVE: Rapid eye movement sleep behavior disorder (RBD) frequently occurs in Parkinson's disease (PD), however, the exact pathophysiological mechanism underlying its occurrence is not clear. In this study, we explored whether there is abnormal spontaneous neuronal activities and connectivity maps in some brain areas under resting-state in PD patients with RBD. METHODS: We recruited 38 PD patients (19 PD with RBD and 19 PD without RBD), and 20 age- and gender-matched normal controls. We used resting-state functional magnetic resonance imaging (RS-fMRI) to analyze regional homogeneity (ReHo) and functional connectivity (FC), and further to reveal the neuronal activity in all subjects. RESULTS: Compared with the PD without RBD patients, the PD with RBD patients showed a significant increase in regional homogeneity in the left cerebellum, the right middle occipital region and the left middle temporal region, and decreased regional homogeneity in the left middle frontal region. The REM sleep behavioral disorders questionnaire scores were significantly positively correlated with the ReHo values of the left cerebellum. The functional connectivity analysis in which the four regions described above were used as regions of interest revealed increased functional activity between the left cerebellum and bilateral occipital regions, bilateral temporal regions and bilateral supplementary motor area. CONCLUSION: The pathophysiological mechanism of PD with RBD may be related to abnormal spontaneous neuronal activity patterns with strong synchronization of cerebellar and visual-motor relevant cortex, and the increased connectivity of the cerebellum with the occipital and motor regions.