ArticleAnnals of clinical and translational neurology2019
A data mining approach for classification of orthostatic and essential tremor based on MRI-derived brain volume and cortical thickness.
Article in Annals of clinical and translational neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Increased susceptibility and volume reduction in deep brain nuclei in primary orthostatic tremor.BMJ neurology open · 2026Article
- Artificial Intelligence in the Diagnosis and Quantitative Phenotyping of Hyperkinetic Movement Disorders: A Systematic Review.Journal of clinical medicine · 2024Review
- Multimodal imaging and electrophysiological study in the differential diagnosis of rest tremor.Frontiers in neurology · 2024Article
- Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor.Frontiers in neurology · 2024Article
- The role of laboratory investigations in the classification of tremors.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2023Review
- Differentiating between common PSP phenotypes using structural MRI: a machine learning study.Journal of neurology · 2023Article
- Cortical involvement in essential tremor with and without rest tremor: a machine learning study.Journal of neurology · 2023Article
- Identification of essential tremor based on resting-state functional connectivity.Human brain mapping · 2023Article
- Combined brain network topological metrics with machine learning algorithms to identify essential tremor.Frontiers in neuroscience · 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
- Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis.Frontiers in human neuroscience · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
objectiveOrthostatic tremor (OT) is an extremely rare, misdiagnosed, and underdiagnosed disorder affecting adults in midlife. There is debate as to whether it is a different condition or a variant of essential tremor (ET), or even, if both conditions coexist. Our objective was to use data mining classification methods, using magnetic resonance imaging (MRI)-derived brain volume and cortical thickness data, to identify morphometric measures that help to discriminate OT patients from those with ET.
methodsMRI-derived brain volume and cortical thickness were obtained from 14 OT patients and 15 age-, sex-, and education-matched ET patients. Feature selection and machine learning methods were subsequently applied.
resultsFour MRI features alone distinguished the two, OT from ET, with 100% diagnostic accuracy. More specifically, left thalamus proper volume (normalized by the total intracranial volume), right superior parietal volume, right superior parietal thickness, and right inferior parietal roughness (i.e., the standard deviation of cortical thickness) were shown to play a key role in OT and ET characterization. Finally, the left caudal anterior cingulate thickness and the left caudal middle frontal roughness allowed us to separate with 100% diagnostic accuracy subgroups of OT patients (primary and those with mild parkinsonian signs).
conclusionsA data mining approach applied to MRI-derived brain volume and cortical thickness data may differentiate between these two types of tremor with an accuracy of 100%. Our results suggest that OT and ET are distinct conditions.
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