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Sci Rep. 2017 May 19;7(1):2190. doi: 10.1038/s41598-017-02122-3.

A data mining approach using cortical thickness for diagnosis and characterization of essential tremor.

Author information

1
Neural and Cognitive Engineering group, Automation and Robotics Center (CAR), CSIC-UPM, Arganda del Rey, Spain. jignacio.serrano@csic.es.
2
Faculty of Biosanitary Sciences, Francisco de Vitoria University, Pozuelo de Alarcón, Madrid, Spain.
3
Brain Damage Service, Hospital Beata Maria Ana, Madrid, Spain.
4
Neural and Cognitive Engineering group, Automation and Robotics Center (CAR), CSIC-UPM, Arganda del Rey, Spain.
5
Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
6
Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA.
7
Center for Neuroepidemiology and Clinical Neurological Research, Yale School of Medicine and Yale School of Public Health, New Haven, CT, USA.
8
Department of Neurology, Center of Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), University Hospital 12 de Octubre, Madrid, Spain. jbenitol67@gmail.com.
9
Department of Medicine, Faculty of Medicine Complutense University, Madrid, Spain. jbenitol67@gmail.com.

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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