ArticleScientific reports2017
A data mining approach using cortical thickness for diagnosis and characterization of essential tremor.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- The Contribution of Neuroimaging to the Understanding of Essential Tremor Pathophysiology: a Systematic Review.Cerebellum (London, England) · 2022Pooled it
- Imaging the Pathophysiology of Essential Tremor-A Systematic Review.Frontiers in neurology · 2021Pooled it
- Neuroscience of human ventral lateral thalamic nucleus related to movement and movement disorders.Journal of neurophysiology · 2025Review
- Unraveling morphological brain network disparities Parkinsonian tremor from essential tremor: an artificial intelligence approach for clinical differentiation.NPJ Parkinson's disease · 2025Article
- Changes in glucose metabolism in essential tremor: within and beyond the cerebello-thalamo-cortical circuit.Brain communications · 2025Article
- Artificial Intelligence in the Diagnosis and Quantitative Phenotyping of Hyperkinetic Movement Disorders: A Systematic Review.Journal of clinical medicine · 2024Review
- Dynamic functional changes upon thalamotomy in essential tremor depend on baseline brain morphometry.Scientific reports · 2024Article
- Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor.Frontiers in neurology · 2024Article
- Cortical involvement in essential tremor with and without rest tremor: a machine learning study.Journal of neurology · 2023Article
- Brain microstructural changes in essential tremor patients and correlations with clinical characteristics: a diffusion kurtosis imaging study.Journal of neurology · 2023Article
- Reproducibility of cerebellar involvement as quantified by consensus structural MRI biomarkers in advanced essential tremor.Scientific reports · 2023Article
- Exploring the heterogeneous morphometric data in essential tremor with probabilistic modelling.NeuroImage. Clinical · 2023Article
- Morphometric features of drug-resistant essential tremor and recovery after stereotactic radiosurgical thalamotomy.Network neuroscience (Cambridge, Mass.) · 2022Article
- Topologically Disrupted Gray Matter Networks in Drug-Naïve Essential Tremor Patients With Poor Sleep Quality.Frontiers in neurology · 2022Article
- Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study.Journal of medical Internet research · 2021Observational
- A deep explainable artificial intelligent framework for neurological disorders discrimination.Scientific reports · 2021Article
- Analysis of Fine Motor Skills in Essential Tremor: Combining Neuroimaging and Handwriting Biomarkers for Early Management.Frontiers in human neuroscience · 2021Article
- Neural Representations of the Voice Tremor Spectrum.Movement disorders : official journal of the Movement Disorder Society · 2020Article
- PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models.Scientific reports · 2020Article
- A data mining approach for classification of orthostatic and essential tremor based on MRI-derived brain volume and cortical thickness.Annals of clinical and translational neurology · 2019Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
Essential tremor (ET) is one of the most prevalent movement disorders. Being that it is a common disorder, its diagnosis is considered routine. However, misdiagnoses may occur regularly. Over the past decade, several studies have identified brain morphometric changes in ET, but these changes remain poorly understood. Here, we tested the informativeness of measuring cortical thickness for the purposes of ET diagnosis, applying feature selection and machine learning methods to a study sample of 18 patients with ET and 18 age- and sex-matched healthy control subjects. We found that cortical thickness features alone distinguished the two, ET from controls, with 81% diagnostic accuracy. More specifically, roughness (i.e., the standard deviation of cortical thickness) of the right inferior parietal and right fusiform areas was shown to play a key role in ET characterization. Moreover, these features allowed us to identify subgroups of ET patients as well as healthy subjects at risk for ET. Since treatment of tremors is disease specific, accurate and early diagnosis plays an important role in tremor management. Supporting the clinical diagnosis with novel computer approaches based on the objective evaluation of neuroimage data, like the one presented here, may represent a significant step in this direction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.