Deep cervical lymph nodes in Parkinson's disease and atypical Parkinson's disease: A potential ultrasound biomarker for differential diagnosis.

Background: Parkinson's disease (PD) is a common degenerative disease caused by abnormal accumulation of α-synuclein. The glymphatic pathway is essential for removing macromolecular proteins including α-synuclein from the brain, which flows into deep cervical lymph nodes (DCLNs) through meningeal lymphatics. As a terminal station for the cerebral lymphatic system drainage, DCLNs can be easily assessed clinically. Objectives: Although the drainage function of the cerebral lymphatic system is impaired in PD, the correlation between DCLNs and PD remains unknown. Design: Single-center retrospective cross-sectional study. Methods: The size of the DCLNs were measured using ultrasound. The Movement Disorder Society Sponsored Revision Unified Parkinson's Disease Rating Scale and other scales were used to assess PD motor and non-motor symptoms. Results: Compared with the healthy control (HC) and the atypical Parkinson's disease (AP) groups, the size of the second and third DCLNs in the Parkinson's disease (PD) group was significantly smaller (P < .05). The width diameter of the third DCLN (DCLN3(y)) was significantly smaller in the PD group than in the AP group (P = .014). DCLN3(y) combined with a variety of clinical features improved the sensitivity of AP identification (sensitivity = .813). Conclusion: DCLNs were able to distinguish HC, PD and AP and were mainly located in Robbins ΙΙA level. PD and AP were associated with different factors that influenced the size of the DCLNs. DCLN3(y) plays an important role in differentiating PD from AP, which, combined with other clinical features, has the ability to distinguish PD from AP; in particular, the sensitivity of AP diagnosis was improved.

Deep cervical lymph nodes, a potential marker for the diagnosis of PD and AP. Deep cervical lymph nodes (DCLNs) were able to distinguish HC, PD and AP and were mainly located in Robbins ΙΙA level. PD and AP were associated with different factors that influenced the size of the DCLNs. DCLN3(y) plays an important role in differentiating PD from AP, which, combined with other clinical features, has the ability to distinguish PD from AP; in particular, the sensitivity of AP diagnosis was improved. DCLN has potential diagnostic value in differentiating PD from AP.

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.

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.

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".