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...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2017-05-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-017-02122-3 |
id |
doaj-6ed9ef8fb6f144339c99229afef1c61a |
---|---|
record_format |
Article |
spelling |
doaj-6ed9ef8fb6f144339c99229afef1c61a2020-12-08T00:19:43ZengNature Publishing GroupScientific Reports2045-23222017-05-017111610.1038/s41598-017-02122-3A data mining approach using cortical thickness for diagnosis and characterization of essential tremorJ. Ignacio Serrano0Juan P. Romero1Ma Dolores del Castillo2Eduardo Rocon3Elan D. Louis4Julián Benito-León5Neural and Cognitive Engineering group, Automation and Robotics Center (CAR), CSIC-UPMFaculty of Biosanitary Sciences, Francisco de Vitoria University, Pozuelo de AlarcónNeural and Cognitive Engineering group, Automation and Robotics Center (CAR), CSIC-UPMNeural and Cognitive Engineering group, Automation and Robotics Center (CAR), CSIC-UPMDepartment of Neurology, Yale School of MedicineDepartment of Neurology, Center of Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), University Hospital 12 de OctubreAbstract 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.https://doi.org/10.1038/s41598-017-02122-3 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
J. Ignacio Serrano Juan P. Romero Ma Dolores del Castillo Eduardo Rocon Elan D. Louis Julián Benito-León |
spellingShingle |
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 Scientific Reports |
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 Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2017-05-01 |
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. |
url |
https://doi.org/10.1038/s41598-017-02122-3 |
work_keys_str_mv |
AT jignacioserrano adataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT juanpromero adataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT madoloresdelcastillo adataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT eduardorocon adataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT elandlouis adataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT julianbenitoleon adataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT jignacioserrano dataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT juanpromero dataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT madoloresdelcastillo dataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT eduardorocon dataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT elandlouis dataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor AT julianbenitoleon dataminingapproachusingcorticalthicknessfordiagnosisandcharacterizationofessentialtremor |
_version_ |
1724396457611493376 |