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© 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Objective

Orthostatic 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.

Methods

MRI‐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.

Results

Four 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).

Conclusions

A 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.

Details

Title
A data mining approach for classification of orthostatic and essential tremor based on MRI‐derived brain volume and cortical thickness
Author
Julián Benito‐León 1   VIAFID ORCID Logo  ; Louis, Elan D 2 ; Virginia Mato‐Abad 3 ; Alvaro Sánchez‐Ferro 4 ; Romero, Juan P 5 ; Matarazzo, Michele 6 ; Serrano, J Ignacio 7 

 Department of Neurology, University Hospital “12 de Octubre”, Madrid, Spain; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain; Department of Medicine, Complutense University, Madrid, Spain 
 Department of Neurology, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Chronic Disease, Epidemiology, Yale School of Public Health, Yale University, New Haven, Connecticut; Center for Neuroepidemiology and Clinical Neurological Research, Yale School of Medicine, Yale University, New Haven, Connecticut 
 ISLA, Faculty of Computer Science, A Coruña University, A Coruña, Spain 
 Department of Neurology, HM CINAC, University Hospital HM Puerta del Sur, Móstoles, Madrid, Spain; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 
 Faculty of Experimental Sciences, Francisco de Vitoria University, Pozuelo de Alarcón, Madrid, Spain; Brain Damage Unit, Hospital Beata Maria Ana, Madrid, Spain 
 Pacific Parkinson's Research Centre and Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada 
 Neural and Cognitive Engineering group, Center for Automation and Robotics, CAR CSIC‐UPM, Arganda del Rey, Madrid, Spain 
Pages
2531-2543
Section
Research Articles
Publication year
2019
Publication date
Dec 2019
Publisher
John Wiley & Sons, Inc.
e-ISSN
23289503
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2327533597
Copyright
© 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.