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.

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.

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.

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.

Dexmedetomidine protects SH-SY5Y cells against MPP+ -induced declining of mitochondrial membrane potential and cell cycle deficits.

Dexmedetomidine (Dex), an adrenergic α2 receptor agonist, is commonly used in deep-brain stimulation surgery for Parkinson's disease (PD). However, there is evidence that the use of anaesthetics may accelerate the progression of neurodegenerative diseases. The effect of Dex on PD remains unclear. Here, we cultured the all-trans-retinoicacid (ATRA) differentiated SH-SY5Y cells in vitro and then treated with MPP+ (1.5mM) with or without Dex (10nM) or Dex combined with Atipamezole (Ati,100nM, adrenergic α2 receptor inhibitor). The ratio of apoptotic cells, mitochondrial membrane potential (Δψm), reactive oxygen species (ROS), cell cycle and apoptotic markers (Cleaved caspase-3, 9) were analysed by flow cytometry and immunofluorescence. We found that the levels of apoptotic ratio and cleaved caspase-3, 9 increased, ROS accumulated, and mitochondrial membrane potential decreased after MPP+treatment, while these changes were partially reversed by Dex. Dex also prevented MPP+ induced cell arrest by increasing G1 phase cells, decreasing S phase cells, and decreasing the expression of cyclinD1 and Cdk4. Moreover the effects of Dex were partially reversed by Ati. These findings reveal that Dex attenuated MPP+ -induced apoptosis of SH-SY5Y cells by preventing the loss of Δψm, reducing ROS, and regulating the cell cycle. Our findings indicated that Dex is more likely to be a potential drug for the treatment of 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.

Altered local and matrix functional connectivity in depressed essential tremor patients.

BACKGROUND: Depression in essential tremor (ET) has been constantly studied and reported, while the associated brain activity changes remain unclear. Recently, regional homogeneity (ReHo), a voxel-wise local functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging, has provided a promising way to observe spontaneous brain activity. METHODS: Local FC analyses were performed in forty-one depressed ET patients, 49 non-depressed ET patients and 43 healthy controls (HCs), and then matrix FC and clinical depression severity correlation analyses were further performed to reveal spontaneous neural activity changes in depressed ET patients. RESULTS: Compared with the non-depressed ET patients, the depressed ET patients showed decreased ReHo in the bilateral cerebellum lobules IX, and increased ReHo in the bilateral anterior cingulate cortices and middle prefrontal cortices. Twenty-five significant changes of ReHo clusters were observed in the depressed ET patients compared with the HCs, and matrix FC analysis further revealed that inter-ROI FC differences were also observed in the frontal-cerebellar-anterior cingulate cortex pathway. Correlation analyses showed that clinical depression severity was positively correlated with the inter-ROI FC values between the anterior cingulate cortex and bilateral middle prefrontal cortices and was negatively correlated with the inter-ROI FC values of the anterior cingulate cortex and bilateral cerebellum lobules IX. CONCLUSION: Our findings revealed local and inter-ROI FC differences in frontal-cerebellar-anterior cingulate cortex circuits in depressed ET patients, and among these regions, the cerebellum lobules IX, middle prefrontal cortices and anterior cingulate cortices could function as pathogenic structures underlying depression in ET patients.

Different functional connectivity modes of the right fronto-insular cortex in akinetic-rigid and tremor-dominant Parkinson's disease.

BACKGROUND: Patients with akinetic-rigid Parkinson's disease (AR-PD) are more prone to cognitive decline and depressive symptoms than tremor-dominant PD (TD-PD) patients. The right fronto-insular cortex (rFIC), as a key node of salience network, plays a critical role in the switching between central executive network and default mode network. In this study, we explored the functional connectivity mode of rFIC with triple-brain networks, namely default mode network, salience network, and central executive network, in two motor subtypes of PD. METHODS: We recruited 44 PD patients (including the TD-PD group and AR-PD group) and 18 age-matched healthy controls (HCs). We performed functional connectivity (FC) analysis of resting-state functional MRI. RESULTS: Compared with TD-PD, decreased FC were found in the right insular cortex and bilateral anterior cingulate gyrus in AR-PD. Compared with HCs, decreased FC in the bilateral insula, the anterior cingulate gyrus, the precentral gyrus, and the right medial frontal gyrus were found; therein, the FC value of rFIC-precentral gyrus was positively correlated with the Unified Parkinson's Disease Rating Scale-II score in AR-PD (p = 0.0482, r = 0.4162). While TD-PD showed decreased FC in the left insula as well as bilateral anterior cingulate gyrus when compared with HCs, and the FC value of the rFIC-left insula was positively correlated with its Hamilton Depression Rating Scale score (p = 0.02, r = 0.50). CONCLUSION: The functional connectivity mode of rFIC in AR-PD differed from that in TD-PD. The decreased rFIC FC with the other nodes of salience network might be a potential indicator for AR-PD patients prone to develop cognitive decline and depressive symptoms.

Activation of liver X receptor promotes hippocampal neurogenesis and improves long-term cognitive function recovery in acute cerebral ischemia-reperfusion mice.

Cerebral ischemia (CI) leads to cognitive dysfunction due to the loss of hippocampal neurons. Liver X receptors (LXRs), including the LXRα and LXRß isoforms, are critical for neurogenesis, synaptic plasticity, neurodegeneration, and cholesterol metabolism. However, the potential role of LXRs in the pathogenesis of CI-induced cognitive impairment is unclear. Therefore, we investigated the effects of LXR activation on hippocampal neurogenesis and cognitive function in mice with CI. C57 mice were randomized into four groups that included a sham group and three treatment groups with CI [Vehicle, TO901317 (TO90, an agonist of LXRs) and GSK2033 (an antagonist of LXRs)]. Mice were subjected to bilateral common carotid artery occlusion for 20 min to induce transient CI. The Morris water maze test was executed to detect spatial learning and memory. Proliferation, differentiation, and immature neurons in the subgranular zone (SGZ) were examined using Immunofluorescence. Western blot assay was used to detect the expression of the Wnt/ß-catenin signaling pathway-associated protein. TO90 significantly improved spatial learning and memory deficits induced by CI on 28 days. It enhanced the proliferation of neural stem cells, the number of immature neurons and the differentiation from nascent cells to neurons. The expression of the Wnt/ß-catenin signaling pathway-associated protein level was totally increased. The forenamed effects of TO90 were decreased in GSK2033 group. Thus, our findings suggest that LXRs activation can improve long-term cognitive dysfunction caused by CI by increasing neurogenesis, and LXRs may serve as a potential therapeutic target for cerebral ischemia. Cover Image for this issue: doi: 10.1111/jnc.14753.

Effects of PPARs/20-HETE on the renal impairment under diabetic conditions.

Diabetic nephropathy (DN) is one of the most severe complications of diabetes mellitus. The pathomolecular events behind DN remain uncertain. Peroxisome proliferator-activated receptors (PPARs) play essential functions in the development of DN. Meanwhile, 20-hydroxyeicosatetraenoic acid (20-HETE) also plays central roles in the regulation of renal function. However, the relationship between PPARs and 20-HETE is rarely studied in DN. It was revealed in our study that both PPARs expression and CYP4A-20-HETE level were decreased under DN conditions in vivo and in vitro. Supplementation with bezafibrate, a PPAR pan-agonist, improved the damage of kidney in DN mice and in high glucose-induced NRK-52E cells, following the up-regulation of PPARs and the increase of CYP4A-20-HETE. PPARα antagonist (MK886), PPARß antagonist (GSK0660), and PPARγ antagonist (GW9662) reversed the protection of bezafibrate in NRK-52E, and abrogated the up-regulation of CYP4A-20-HETE produced by bezafibrate. Noteworthily, 20-HETE synthetase inhibitor, HET0016, also blocked the bezafibrate-mediated improvement of NRK-52E, and abolished the up-regulation of PPARs expression. Collectively, our data suggest that the concurrent down-regulation and interaction of PPARs and 20-HETE play crucial roles in the pathogenesis process of DN, and we provide a novel evidence that PPARs/20-HETE signaling may be served as a therapeutic target for DN patients.

[Gastrodin improves hippocampal neurogenesis by NO-cGMP-PKG signaling pathway in cerebral ischemic mice].

This paper was aimed to investigate the effect of gastrodin( GAS) on hippocampal neurogenesis after cerebral was chemic and to explore its mechanism of action related to NO. The cerebral ischemia model of C57 BL/6 mice was established by bilateral common carotid artery occlusion. The pathological changes in hippocampal CA1 region and the cognitive function of mice were assessed by HE staining and Morris water maze test,respectively. The count of Brd U/Neu N positive cells in dentate gyrus was detected by immunofluorescence assay. The NOS activity and the NO content were determined by colorimetric and nitrate reduction methods,respectively.The level of c GMP was measured by ELISA kit,and the PKG protein expression was tested by Western blot. On postoperative day 8,the hippocampal CA1 pyramidal neurons of mice showed irregular structure,with obvious nuclear pyknosis,loose cell arrangement and obvious decrease in the number of neurons. On postoperative day 29,the spatial learning ability and memory were decreased. These results indicated cerebral ischemia in mice. Meanwhile,the Brd U/Neu N positive cells were increased significantly in ischemic mice,indicating that neurogenesis occurred in hippocampus after cerebral ischemia. Treatment with different doses of gastrodin( 50 and 100 mg·kg-1) significantly ameliorated the pathological damages in the CA1 region,improved the ability of learning and memory,and promoted hippocampal neurogenesis. At the same time,both the NOS activity and the NO concentration were decreased in model group,but the c GMP level was increased,and the PKG protein expression was up-regulated. Gastrodin administration activated the NOS activity,promoted NO production,further increased c GMP level and up-regulated PKG protein expression. These results suggested that gastrodin can promote hippocampal neurogenesis after cerebral ischemia and improve cognitive function in mice,which may be related to the activation of NO-cGMP-PKG signaling pathway.

[A partition bagging ensemble learning algorithm for Parkinson's speech data mining].

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.

Analysis of the clinical features of early Parkinson's disease with comparatively integrated intestinal function.

OBJECTIVE: Constipation is among the most frequently delineated nonmotor symptoms (NMS) with a high occurrence in Parkinson's disease (PD). The purpose of our study was to investigate whether PD with comparatively integrated intestinal function (without constipation) in the early stage had different clinical features compared to constipated PD. METHOD: We conducted a study of 105 consecutive de novo as well as early treated (treated for shorter than 3 months), aged 50 years or older outpatients. Subjects were administered motor and nonmotor questionnaires as well as constipation associated examinations. Then, we explored the distinctive features of nonconstipated contrasted to constipated PD by using univariate, multiple regression analysis and correlation analysis. RESULTS: Nonconstipated PD tended to have fewer motor deficits, as well as lower Hoehn and Yahr (H&Y) stage and they mainly presented as tremor-dominant (TD), while constipated group had a higher occurrence of posture instability and gait difficulty (PIGD); nonconstipated patients were inclined to live in urban area, the NMSloads and prevalence of NMS were lower compared to constipated ones. Correlation analysis found a discord between NMSloads and disease severity based on H&Y stage and motor scores in nonconstipated PD. CONCLUSIONS: These results suggest that PD without constipation in early stage may represent a unique clinical phenotype, which may be more benign than PD with constipation.

[Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease].

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.

Essential tremor is associated with disruption of functional connectivity in the ventral intermediate Nucleus--Motor Cortex--Cerebellum circuit.

The clinical benefits of targeting the ventral intermediate nucleus (VIM) for the treatment of tremors in essential tremor (ET) patients suggest that the VIM is a key hub in the network of tremor generation and propagation and that the VIM can be considered as a seed region to study the tremor network. However, little is known about the central tremor network in ET patients. Twenty-six ET patients and 26 matched healthy controls (HCs) were included in this study. After considering structural and head-motion factors and establishing the accuracy of our seed region, a VIM seed-based functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging (RS-fMRI) data was performed to characterize the VIM FC network in ET patients. We found that ET patients and HCs shared a similar VIM FC network that was generally consistent with the VIM anatomical connectivity network inferred from normal nonhuman primates and healthy humans. Compared with HCs, ET patients displayed VIM-related FC changes, primarily within the VIM-motor cortex (MC)-cerebellum (CBLM) circuit, which included decreased FC in the CBLM and increased FC in the MC. Importantly, tremor severity correlated with these FC changes. These findings provide the first evidence that the pathological tremors observed in ET patients might be based on a physiologically pre-existing VIM - MC - CBLM network and that disruption of FC in this physiological network is associated with ET. Further, these findings demonstrate a potential approach for elucidating the neural network mechanisms underlying this disease.

Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor.

INTRODUCTION: The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. METHODS: Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. RESULTS: Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. CONCLUSIONS: The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome.

Statins for Treating Alzheimer's Disease: Truly Ineffective?

BACKGROUND: Animal studies suggest that statins may have a protective effect on Alzheimer's disease (AD), but some clinical trials have reported negative results. OBJECTIVE: To evaluate the efficacy and safety of statins in the treatment of AD. METHODS: We searched the PubMed, Cochrane Library, EMBASE, OVID SP, and ProQuest databases to select double-blind, randomized controlled trials pertaining to statins given to patients diagnosed with AD. The meta-analysis used main outcomes such as scores on the Mini Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale (ADAS-Cog) and secondary outcomes such as scores on the Dependence Scale, Activities of Daily Living Scale (ADCS-ADL), Neuropsychiatric Inventory (NPI), Clinical Global Impression of Change (CGIC), and Alzheimer's Disease Functional Assessment and Change Scale (ADFACS). Safety measures included the standard reporting of any adverse events or laboratory abnormalities. RESULTS: Four studies (1,127 participants) involving patients with a diagnosis of probable or possible AD were included. There were no significant differences between the statins and placebo groups regarding the main outcomes, secondary outcomes, or adverse events. Most of the studies ignored or downplayed risk factors for cerebral vascular disease. CONCLUSION: Statins are well tolerated without unexpected adverse events. However, more research is needed to determine whether statins are effective for AD with vascular risk factors.

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.