Magnetic resonance-guided focused ultrasound surgery (MRgFUS) thalamotomy is an emerging technique for medication-refractory essential tremor (ET), but with variable outcomes. This study used pattern regression analysis to identify brain signatures predictive of tremor improvements. Fifty-four ET patients (mean age = 63.06 years, standard deviation (SD) = 10.55 years, 38 males) underwent unilateral MRgFUS thalamotomy and were scanned for resting-state functional magnetic resonance imaging (rs-fMRI). Seventy-four healthy controls (mean age = 58.09 years, SD = 10.30 years, 38 males) were recruited for comparison. Tremor responses at 12 months posttreatment were evaluated by the Clinical Rating Scale for Tremor. The fractional amplitude of low-frequency fluctuations (fALFF) was calculated from rs-fMRI data. Two-sample t-test was used to generate a disease-specific mask, within which Multivariate Kernel Ridge Regression analyses were conducted. Predicted and actual clinical scores were compared using Pearson\'s correlation coefficient (r) and normalized mean squared error (Norm. MSE). Permutation test and leave-one-out strategy were applied for results validation. KRR identified fALFF patterns that significantly predicted the hand tremor improvement (r = 0.23, P = 0.025; Norm. MSE = 0.05, P = 0.026) and the postural tremor improvement (r = 0.28, P = 0.025; Norm. MSE = 0.06, P = 0.023), but not action tremor improvement. Lobule VI of right cerebellum (Cerebelum_6_R), right superior occipital gyrus (Occipital_Sup_R) and lobule X of vermis (Vermis_10) contributed most for hand tremor prediction (normalized weights (NW): 2.77%, 2.40%, 2.34%) while Vermis_10, left supplementary motor area (Supp_Motor_Area_L) and right hippocampus (Hippocampus_R) for postural tremor prediction (NW: 2.69%, 2.12%, 2.05%). The low contributing NW of the individual brain regions suggested that the fALFF pattern as a whole is an overall predicting feature. Preoperative fALFF pattern predicts tremor benefits induced by MRgFUS thalamotomy. ClinicalTrials.gov number: NCT04570046.

UNASSIGNED: ET, one of the most prevalent neurological disorders, presents a significant challenge in terms of disability. Despite the growing focus on ET in recent years, comprehensive bibliometric analysis has been lacking.
UNASSIGNED: This study delves into essential tremor research covering the period from 2013 to 2023, utilizing the Web of Science (WOS) database. Employing CiteSpace for quantitative analysis, it examines an array of metrics including annual publication trends, contributions from countries and institutions, authorship patterns, key terminologies, and patterns of reference co-citation. The primary objective is to use CiteSpace for a detailed visual exploration of the literature over the last decade, pinpointing the evolving landscape and key areas of focus in essential tremor research, and thus providing a foundation for future investigative endeavors.
UNASSIGNED: There were 2,224 literary works included in all. The amount of published works has been steadily rising in recent years. Of them, the majority originate from the United States, Louis, Elan D. is the publisher of the most publications (161 articles), and Movement Disorders is the journal that receives the most citations. The key words contribution and co-cited literatures suggest that the main research hotspots in recent years are the physiological and pathological mechanism of essential tremor, the determination of optimal targets for deep brain stimulation (DBS) and surgery transcranial magnetic resonance-guided focused ultrasound (MRgFUS) in the surgical management of essential tremor and the innovative research of botulinum toxin administration method.

There is an obvious clinical-pathological overlap between essential tremor and some known tremor-associated short tandem repeat expansion disorders. The aim is to analyse whether these short tandem repeat genes, including ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, ATXN8OS, ATXN10, PPP2R2B, TBP, BEAN1, NOP56, DAB1, ATN1, SADM12 and FMR1, are associated with familial essential tremor patients. Genetic analysis of repeat sizes in tremor-associated short tandem repeat expansions was performed in a large cohort of 515 familial essential tremor probands and 300 controls. The demographic and clinical features among carriers of pathogenic expansions, intermediate repeats and non-carriers were compared. A total of 18 out of 515 (18/515, 3.7%) patients were found to have repeats expansions, including 12 cases (12/515, 2.5%) with intermediate repeat expansions (one ATXN1, eight TBP, two FMR1, one ATN1), and six cases (6/515, 1.2%) with pathogenic expansions (one ATXN1, one ATXN2, one ATXN8OS, one PPP2R2B, one FMR1, one SAMD12). There were no statistically significant differences in intermediate repeats compared to healthy controls. Furthermore, there were no significant differences in demographics and clinical features among individuals with pathogenic expansions, intermediate repeat expansions carriers and non-carriers. Our study indicates that the intermediate repeat expansion in tremor-associated short tandem repeat expansions does not pose an increased risk for essential tremor, and rare pathogenic expansion carriers have been found in the familial essential tremor cohort. The diagnosis of essential tremor based solely on clinical symptoms remains a challenge in distinguishing it from known short tandem repeat expansions diseases with overlapping clinical-pathological features.

UNASSIGNED: The tremor characteristics of patients with spinocerebellar ataxia 12 (SCA12) are often likened to those in patients with essential tremor (ET); however, data are sparse, and videotaped tremor examinations are rare.
UNASSIGNED: A 37-year-old woman with progressive hand and head tremors underwent genetic testing after conventional diagnostics failed to explain her symptoms. A PPP2R2B variation confirmed spinocerebellar ataxia type 12 (SCA12), a condition not previously considered because classical cerebellar signs were absent. The tremor characteristics of this patient differed in numerous respects from those seen in patients with ET.
UNASSIGNED: Although often likened to ET, under careful scrutiny, the tremor characteristics observed in this patient with SCA12 were inconsistent with those typically seen in ET. Such discrepancies highlight the necessity of careful phenotyping for tremor disorders, particularly in familial cases. Recognizing the specific tremor phenomenology of SCA12 and distinguishing it from ET is crucial to avoid misdiagnosis and to guide appropriate management and familial counseling.
UNASSIGNED: This report characterizes in detail an early-stage SCA12 patient initially misdiagnosed as essential tremor, underscoring the importance of nuanced clinical assessment and genetic testing in atypical tremor cases. Similar patients should be meticulously phenotyped to prevent misclassification and enhance our understanding of tremor pathophysiology.

Essential tremor (ET) stands as the most prevalent movement disorder, characterized by rhythmic and involuntary shaking of body parts. Achieving an accurate and comprehensive assessment of tremor severity is crucial for effectively diagnosing and managing ET. Traditional methods rely on clinical observation and rating scales, which may introduce subjective biases and hinder continuous evaluation of disease progression. Recent research has explored new approaches to quantifying ET. A promising method involves the use of intelligent devices to facilitate objective and quantitative measurements. These devices include inertial measurement units, electromyography, video equipment, and electronic handwriting boards, and more. Their deployment enables real-time monitoring of human activity data, featuring portability and efficiency. This capability allows for more extensive research in this field and supports the shift from in-lab/clinic to in-home monitoring of ET symptoms. Therefore, this review provides an in-depth analysis of the application, current development, potential characteristics, and roles of intelligent devices in assessing ET.

UNASSIGNED: Parkinson\'s disease (PD) and Essential tremor (ET) are the two most common tremor diseases with recognized genetic pathogenesis. The overlapping clinical features suggest they may share genetic predispositions. Our previous study systematically investigated the association between rare coding variants in ET-associated genes and early-onset PD (EOPD), and found the suggestive association between teneurin transmembrane protein 4 (TENM4) and EOPD. In the current research, we explored the potential genetic interplay between ET-associated genetic loci/genes and sporadic late-onset PD (LOPD).
UNASSIGNED: We performed whole-genome sequencing in the 1962 sporadic LOPD cases and 1279 controls from mainland China. We first used logistic regression analysis to test the top 16 SNPs identified by the ET genome-wide association study for the association between ET and LOPD. Then we applied the optimized sequence kernel association testing to explore the rare variant burden of 33 ET-associated genes in this cohort.
UNASSIGNED: We did not observe a significant association between the included SNPs with LOPD. We also did not discover a significant burden of rare deleterious variants of ET-associated genes in association with LOPD risk.
UNASSIGNED: Our results do not support the role of ET-associated genetic loci and variants in LOPD.
UNASSIGNED: 1962 cases and 1279 controls were recruited to study the potential genetic interplay between ET-associated genetic loci/variants and sporadic LOPD.No significant association between the ET-associated SNPs and LOPD were observed.No significant burden of rare deleterious variants of ET-associated gene in LOPD risk were found.

UNASSIGNED: Previous studies have demonstrated the importance of the locus coeruleus (LC) in sleep-wake regulation. Both essential tremor (ET) and Parkinson\'s disease (PD) share common sleep disorders, such as poor quality of sleep (QoS). LC pathology is a feature of both diseases. A question arises regarding the contribution of LC degeneration to the occurrence of poor QoS.
UNASSIGNED: To evaluate the association between LC impairment and sleep disorders in ET and PD patients.
UNASSIGNED: A total of 83 patients with ET, 124 with PD, and 83 healthy individuals were recruited and divided into ET/PD with/without poor QoS (Sle/NorET and Sle/NorPD) subgroups according to individual Pittsburgh Sleep Quality Index (PSQI) score. Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and free-water imaging derived from diffusion MRI were performed. Subsequently, we evaluated the association between contrast-to-noise ratio of LC (CNRLC) and free-water value of LC (FWLC) with PSQI scores in ET and PD groups.
UNASSIGNED: CNRLC was significantly lower in ET (p = 0.047) and PD (p = 0.018) than in healthy individuals, whereas no significant difference was found in FWLC among the groups. No significant differences were observed in CNR/FWLC between patients with/without sleep disorders after multiple comparison correction. No correlation was identified between CNR/FWLC and PSQI in ET and PD patients.
UNASSIGNED: LC degeneration was observed in both ET and PD patients, implicating its involvement in the pathophysiology of both diseases. Additionally, no significant association was observed between LC integrity and PSQI, suggesting that LC impairment might not directly relate to overall QoS.

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.
CONCLUSIONS: 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.

BACKGROUND: MR-guided focused ultrasound (MRgFUS) thalamotomy is a novel and effective treatment for medication-refractory tremor in essential tremor (ET), but how the brain responds to this deliberate lesion is not clear.
OBJECTIVE: The current study aimed to evaluate the immediate and longitudinal alterations of functional networks after MRgFUS thalamotomy.
METHODS: We retrospectively obtained preoperative and postoperative 30-day, 90-day, and 180-day data of 31 ET patients subjected with MRgFUS thalamotomy from 2018 to 2020. Their archived resting-state functional MRI data were used for functional network comparison as well as graph-theory metrics analysis. Both partial least squares (PLS) regression and linear regression were conducted to associate functional features to tremor symptoms.
RESULTS: MRgFUS thalamotomy dramatically abolished tremors, while global functional network only sustained immediate fluctuation within one week after the surgery. Network-based statistics have identified a long-term enhanced corticostriatal subnetwork by comparison between 180-day and preoperative data (P = 0.019). Within this subnetwork, network degree, global efficiency and transitivity were significantly recovered in ET patients right after MRgFUS thalamotomy compared to the pre-operative timepoint (P < 0.05), as well as hemisphere lateralization (P < 0.001). The PLS main component significantly accounted for 33.68 % and 34.16 % of the total variances of hand tremor score and clinical rating scale for tremor (CRST)-total score (P = 0.037 and 0.027). Network transitivity of this subnetwork could serve as a reliable biomarker for hand tremor score control prediction at 180-day after the surgery (β = 2.94, P = 0.03).
CONCLUSIONS: MRgFUS thalamotomy promoted corticostriatal connectivity activation correlated with tremor improvement in ET patient after MRgFUS thalamotomy.

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.