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

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 change...

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Autores principales:	J. Ignacio Serrano, Juan P. Romero, Ma Dolores del Castillo, Eduardo Rocon, Elan D. Louis, Julián Benito-León
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6ed9ef8fb6f144339c99229afef1c61a
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spelling	oai:doaj.org-article:6ed9ef8fb6f144339c99229afef1c61a2021-12-02T15:06:18ZA data mining approach using cortical thickness for diagnosis and characterization of essential tremor10.1038/s41598-017-02122-32045-2322https://doaj.org/article/6ed9ef8fb6f144339c99229afef1c61a2017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02122-3https://doaj.org/toc/2045-2322Abstract 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.J. Ignacio SerranoJuan P. RomeroMa Dolores del CastilloEduardo RoconElan D. LouisJulián Benito-LeónNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-16 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q J. Ignacio Serrano Juan P. Romero Ma Dolores del Castillo Eduardo Rocon Elan D. Louis Julián Benito-León A data mining approach using cortical thickness for diagnosis and characterization of essential tremor
description	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.
format	article
author	J. Ignacio Serrano Juan P. Romero Ma Dolores del Castillo Eduardo Rocon Elan D. Louis Julián Benito-León
author_facet	J. Ignacio Serrano Juan P. Romero Ma Dolores del Castillo Eduardo Rocon Elan D. Louis Julián Benito-León
author_sort	J. Ignacio Serrano
title	A data mining approach using cortical thickness for diagnosis and characterization of essential tremor
title_short	A data mining approach using cortical thickness for diagnosis and characterization of essential tremor
title_full	A data mining approach using cortical thickness for diagnosis and characterization of essential tremor
title_fullStr	A data mining approach using cortical thickness for diagnosis and characterization of essential tremor
title_full_unstemmed	A data mining approach using cortical thickness for diagnosis and characterization of essential tremor
title_sort	data mining approach using cortical thickness for diagnosis and characterization of essential tremor
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/6ed9ef8fb6f144339c99229afef1c61a
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A data mining approach using cortical thickness for diagnosis and characterization of essential tremor

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