Atrophy of cerebellar peduncles in essential tremor: a machine learning-based volumetric analysis

Cortical involvement in essential tremor with and without rest tremor: a machine learning study

INTRODUCTION

There is some debate on the relationship between essential tremor with rest tremor (rET) and the classic ET syndrome, and only few MRI studies compared ET and rET patients. This study aimed to explore structural cortical differences between ET and rET, to improve the knowledge of these tremor syndromes.

METHODS

Thirty-three ET patients, 30 rET patients and 45 control subjects (HC) were enrolled. Several MR morphometric variables (thickness, surface area, volume, roughness, mean curvature) of brain cortical regions were extracted using Freesurfer on T1-weighted images and compared among groups. The performance of a machine learning approach (XGBoost) using the extracted morphometric features was tested in discriminating between ET and rET patients.

RESULTS

rET patients showed increased roughness and mean curvature in some fronto-temporal areas compared with HC and ET, and these metrics significantly correlated with cognitive scores. Cortical volume in the left pars opercularis was also lower in rET than in ET patients. No differences were found between ET and HC. XGBoost discriminated between rET and ET with mean AUC of 0.86 ± 0.11 in cross-validation analysis, using a model based on cortical volume. Cortical volume in the left pars opercularis was the most informative feature for classification between the two ET groups.

CONCLUSION

Our study demonstrated higher cortical involvement in fronto-temporal areas in rET than in ET patients, which may be linked to the cognitive status. A machine learning approach based on MR volumetric data demonstrated that these two ET subtypes can be distinguished using structural cortical features.

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.

Exploring the heterogeneous morphometric data in essential tremor with probabilistic modelling

Essential tremor (ET) is a prevalent movement disorder characterized by marked clinical heterogeneity. Here, we explored the morphometric underpinnings of this cross-subject variability on a cohort of 34 patients with right-dominant drug-resistant ET and 29 matched healthy controls (HCs). For each brain region, group-wise morphometric data was modelled by a multivariate Gaussian to account for morphometric features' (co)variance. No group differences were found in terms of mean values, highlighting the limits of more basic group comparison approaches. Variance in surface area was higher in ET in the left lingual and caudal anterior cingulate cortices, while variance in mean curvature was lower in the right superior temporal cortex and pars triangularis, left supramarginal gyrus and bilateral paracentral gyrus. Heterogeneity further extended to the right putamen, for which a mixture of two Gaussians fitted the ET data better than a single one. Partial Least Squares analysis revealed the rich clinical relevance of the ET population's heterogeneity: first, increased head tremor and longer symptoms' duration were accompanied by broadly lower cortical gyrification. Second, more severe upper limb tremor and impairments in daily life activities characterized the patients whose morphometric profiles were more atypical compared to the average ET population, irrespective of the exact nature of the alterations. Our results provide candidate morphometric substrates for two different types of clinical variability in ET. They also demonstrate the importance of relying on analytical approaches that can efficiently handle multivariate data and enable to test more sophisticated hypotheses regarding its organization.

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.

Transcriptomic effects of propranolol and primidone converge on molecular pathways relevant to essential tremor

Essential tremor (ET) is one of the most common movement disorders, affecting nearly 5% of individuals over 65 years old. Despite this, few genetic risk loci for ET have been identified. Recent advances in pharmacogenomics have previously been useful to identify disease related molecular targets. Notably, gene expression has proven to be quite successful for the inference of drug response in cell models. We sought to leverage this approach in the context of ET where many patients are responsive to two drugs: propranolol and primidone. In this study, cerebellar DAOY and neural progenitor cells were treated for 5 days with clinical concentrations of propranolol and primidone, after which RNA-sequencing was used to identify convergent differentially expressed genes across treatments. Propranolol was found to affect the expression of genes previously associated with ET and other movement disorders such as TRAPPC11. Pathway enrichment analysis of these convergent drug-targeted genes identified multiple terms related to calcium signaling, endosomal sorting, axon guidance, and neuronal morphology. Furthermore, genes targeted by ET drugs were enriched within cell types having high expression of ET-related genes in both cortical and cerebellar tissues. Altogether, our results highlight potential cellular and molecular mechanisms associated with tremor reduction and identify relevant genetic biomarkers for drug-responsiveness in ET.

Morphometric features of drug-resistant essential tremor and recovery after stereotactic radiosurgical thalamotomy

Essential tremor (ET) is the most common movement disorder. Its neural underpinnings remain unclear. Here, we quantified structural covariance between cortical thickness (CT), surface area (SA), and mean curvature (MC) estimates in patients with ET before and 1 year after ventro-intermediate nucleus stereotactic radiosurgical thalamotomy, and contrasted the observed patterns with those from matched healthy controls. For SA, complex rearrangements within a network of motion-related brain areas characterized patients with ET. This was complemented by MC alterations revolving around the left middle temporal cortex and the disappearance of positive-valued covariance across both modalities in the right fusiform gyrus. Recovery following thalamotomy involved MC readjustments in frontal brain centers, the amygdala, and the insula, capturing nonmotor characteristics of the disease. The appearance of negative-valued CT covariance between the left parahippocampal gyrus and hippocampus was another recovery mechanism involving high-level visual areas. This was complemented by the appearance of negative-valued CT/MC covariance, and positive-valued SA/MC covariance, in the right inferior temporal cortex and bilateral fusiform gyrus. Our results demonstrate that different morphometric properties provide complementary information to understand ET, and that their statistical cross-dependences are also valuable. They pinpoint several anatomical features of the disease and highlight routes of recovery following thalamotomy.

Association of Essential Tremor With Novel Risk Loci: A Genome-Wide Association Study and Meta-analysis

Question

Can common genetic variants associated with essential tremor (ET) be identified?

Findings

In this genome-wide association study and meta-analysis including genetic data on 483 054 individuals, 5 genome-wide significant loci were associated with risk of ET and common variants were associated with approximately 18% of ET heritability.

Meaning

Findings of this study may help identify new genes and inform ET biology.

Importance

Essential tremor (ET) is one of the most common movement disorders, affecting 5% of the general population older than 65 years. Common variants are thought to contribute toward susceptibility to ET, but no variants have been robustly identified.

Objective

To identify common genetic factors associated with risk of ET.

Design, Setting, and Participants

Case-control genome-wide association study. Inverse-variance meta-analysis was used to combine cohorts. Multicenter samples collected from European populations were collected from January 2010 to September 2019 as part of an ongoing study. Included patients were clinically diagnosed with or reported having ET. Control individuals were not diagnosed with or reported to have ET. Of 485 250 individuals, data for 483 054 passed data quality control and were used.

Main Outcomes and Measures

Genotypes of common variants associated with risk of ET.

Results

Of the 483 054 individuals included, there were 7177 with ET (3693 [51.46%] female; mean [SD] age, 62.66 [15.12] years), and 475 877 control individuals (253 785 [53.33%] female; mean [SD] age, 56.40 [17.6] years). Five independent genome-wide significant loci and were identified and were associated with approximately 18% of ET heritability. Functional analyses found significant enrichment in the cerebellar hemisphere, cerebellum, and axonogenesis pathways. Genetic correlation (r), which measures the degree of genetic overlap, revealed significant common variant overlap with Parkinson disease (r, 0.28; P = 2.38 × 10⁻⁸) and depression (r, 0.12; P = 9.78 × 10⁻⁴). A separate fine-mapping of transcriptome-wide association hits identified genes such as BACE2, LRRN2, DHRS13, and LINC00323 in disease-relevant brain regions, such as the cerebellum.

Conclusions and Relevance

The results of this genome-wide association study suggest that a portion of ET heritability can be explained by common genetic variation and can help identify new common genetic risk factors for ET.

Essential tremor

Essential tremor (ET) is one of the most common movement disorders, with a reported >60 million affected individuals worldwide. The definition and underlying pathophysiology of ET are contentious. Patients present primarily with motor features such as postural and action tremors, but may also have other non-motor features, including cognitive impairment and neuropsychiatric symptoms. Genetics account for most of the ET risk but environmental factors may also be involved. However, the variable penetrance and challenges in validating data make gene-environment analysis difficult. Structural changes in cerebellar Purkinje cells and neighbouring neuronal populations have been observed in post-mortem studies, and other studies have found GABAergic dysfunction and dysregulation of the cerebellar-thalamic-cortical circuitry. Commonly prescribed medications include propranolol and primidone. Deep brain stimulation and ultrasound thalamotomy are surgical options in patients with medically intractable ET. Further research in post-mortem studies, and animal and cell-based models may help identify new pathophysiological clues and therapeutic targets and, together with advances in omics and machine learning, may facilitate the development of precision medicine for patients with ET.

Disrupted structural connectome and neurocognitive functions in Duchenne muscular dystrophy: classifying and subtyping based on Dp140 dystrophin isoform

OBJECTIVE

Neurocognitive disabilities in Duchenne muscular dystrophy (DMD) children beginning in early childhood and distal DMD gene deletions involving disruption of Dp140 isoform are more likely to manifest significant neurocognitive impairments. MRI data analysis techniques like brain-network metrics can provide information on microstructural integrity and underlying pathophysiology.

METHODS

A prospective study on 95 participants [DMD = 57, and healthy controls (HC) = 38]. The muscular dystrophy functional rating scale (MDFRS) scores, neuropsychology batteries, and multiplex ligand-dependent probe amplification (MLPA) testing were used for clinical assessment, IQ estimation, and genotypic classification. Diffusion MRI and network-based statistics were used to analyze structural connectomes at various levels and correlate with clinical markers.

RESULTS

Motor and executive sub-networks were extracted and analyzed. Out of 57 DMD children, 23 belong to Dp140 + and 34 to Dp140- subgroup. Motor disabilities are pronounced in Dp140- subgroup as reflected by lower MDFRS scores. IQ parameters are significantly low in all-DMD cases; however, the Dp140- has specifically lowest scores. Significant differences were observed in global efficiency, transitivity, and characteristic path length between HC and DMD. Subgroup analysis demonstrates that the significance is mainly driven by participants with Dp140- than Dp140 + isoform. Finally, a random forest classifier model illustrated an accuracy of 79% between HC and DMD and 90% between DMD- subgroups.

CONCLUSIONS

Current findings demonstrate structural network-based characterization of abnormalities in DMD, especially prominent in Dp140-. Our observations suggest that participants with Dp140 + have relatively intact connectivity while Dp140- show widespread connectivity alterations at global, nodal, and edge levels. This study provides valuable insights supporting the genotype-phenotype correlation of brain-behavior involvement in DMD children.

Imaging the Pathophysiology of Essential Tremor-A Systematic Review

Background: The pathophysiology underlying essential tremor (ET) still is poorly understood. Recent research suggests a pivotal role of the cerebellum in tremor genesis, and an ongoing controversy remains as to whether ET constitutes a neurodegenerative disorder. In addition, mounting evidence indicates that alterations in the gamma-aminobutyric acid neurotransmitter system are involved in ET pathophysiology. Here, we systematically review structural, functional, and metabolic neuroimaging studies and discuss current concepts of ET pathophysiology from an imaging perspective.

Methods: We conducted a PubMed and Scopus search from 1966 up to December 2020, entering essential tremor in combination with any of the following search terms and their corresponding abbreviations: positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and gamma-aminobutyric acid (GABA).

Results: Altered functional connectivity in the cerebellum and cerebello-thalamico-cortical circuitry is a prevalent finding in functional imaging studies. Reports from structural imaging studies are less consistent, and there is no clear evidence for cerebellar neurodegeneration. However, diffusion tensor imaging robustly points toward microstructural cerebellar changes. Radiotracer imaging suggests that the dopaminergic axis is largely preserved in ET. Similarly, measurements of nigral iron content and neuromelanin are unremarkable in most studies; this is in contrast to Parkinson's disease (PD). PET and MRS studies provide limited evidence for cerebellar and thalamic GABAergic dysfunction.

Conclusions: There is robust evidence indicating that the cerebellum plays a key role within a multiple oscillator tremor network which underlies tremor genesis. However, whether cerebellar dysfunction relies on a neurodegenerative process remains unclear. Dopaminergic and iron imaging do not suggest a substantial overlap of ET with PD pathophysiology. There is limited evidence for alterations of the GABAergic neurotransmitter system in ET. The clinical, demographical, and genetic heterogeneity of ET translates into neuroimaging and likely explains the various inconsistencies reported.

Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements

Tremor is one of the main symptoms of Parkinson's Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients' tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients' tremor from continuous monitoring of the subjects' movement in their natural environment.