TY - JOUR
T1 - A data mining approach for classification of orthostatic and essential tremor based on MRI-derived brain volume and cortical thickness
AU - Benito-León, Julián
AU - Louis, Elan D.
AU - Mato-Abad, Virginia
AU - Sánchez-Ferro, Alvaro
AU - Romero, Juan P.
AU - Matarazzo, Michele
AU - Serrano, J. Ignacio
N1 - Funding Information:
This research was supported by FEDER funds. Benito‐León is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS39422), European Commission (grant ICT‐2011‐287739, NeuroTREMOR), the Ministry of Economy and Competitiveness (grant RTC‐2015‐3967‐1, NetMD – platform for the tracking of movement disorder), and the Spanish Health Research Agency (grant FIS PI12/01602 and grant FIS PI16/00451). Romero is supported by the European Commission (grant ICT‐2011‐287739, NeuroTREMOR) and the Spanish Ministry of Economy and Competitiveness (grant DPI‐2015‐68664‐C4‐1‐R, NeuroMOD). Louis has received research support from the National Institutes of Health: NINDS #R01 NS094607 (principal investigator), NINDS #R01 NS085136 (principal investigator), NINDS #R01 NS073872 (principal investigator), NINDS #R01 NS085136 (principal investigator), and NINDS #R01 NS088257 (principal investigator). He has also received support from the Claire O'Neil Essential Tremor Research Fund (Yale University). Sánchez‐Ferro is supported by the Consejería de Educación, Juventud y Deporte de la Comunidad de Madrid and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007‐2013). Serrano is supported by RoboCity2030‐DIH‐CM Madrid Robotics Digital Innovation Hub (“Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV”; S2018/NMT‐4331), funded by “Programas de Actividades I + D en la Comunidad de Madrid” and cofunded by Structural Funds of the European Commission. We acknowledge the neuropsychologist Verónica Puertas‐Martín, for her assistance with the project.
Funding Information:
This research was supported by FEDER funds. Benito‐León is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS39422), European Commission (grant ICT‐2011‐287739, NeuroTREMOR), the Ministry of Economy and Competitiveness (grant RTC‐2015‐3967‐1, NetMD – platform for the tracking of movement disorder), and the Spanish Health Research Agency (grant FIS PI12/01602 and grant FIS PI16/00451). Romero is supported by the European Commission (grant ICT‐2011‐287739, NeuroTREMOR) and the Spanish Ministry of Economy and Competitiveness (grant DPI‐2015‐68664‐C4‐1‐R, NeuroMOD). Louis has received research support from the National Institutes of Health: NINDS #R01 NS094607 (principal investigator), NINDS #R01 NS085136 (principal investigator), NINDS #R01 NS073872 (principal investigator), NINDS #R01 NS085136 (principal investigator), and NINDS #R01 NS088257 (principal investigator). He has also received support from the Claire O'Neil Essential Tremor Research Fund (Yale University). Sánchez‐Ferro is supported by the Consejería de Educación, Juventud y Deporte de la Comunidad de Madrid and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007‐2013). Serrano is supported by RoboCity2030‐DIH‐CM Madrid Robotics Digital Innovation Hub (“Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV”; S2018/NMT‐4331), funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the European Commission.
Funding Information:
Funding information This research was supported by FEDER funds. Benito-Le?n is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS39422), European Commission (grant ICT-2011-287739, NeuroTREMOR), the Ministry of Economy and Competitiveness (grant RTC-2015-3967-1, NetMD ? platform for the tracking of movement disorder), and the Spanish Health Research Agency (grant FIS PI12/01602 and grant FIS PI16/00451). Romero is supported by the European Commission (grant ICT-2011-287739, NeuroTREMOR) and the Spanish Ministry of Economy and Competitiveness (grant DPI-2015-68664-C4-1-R, NeuroMOD). Louis has received research support from the National Institutes of Health: NINDS #R01 NS094607 (principal investigator), NINDS #R01 NS085136 (principal investigator), NINDS #R01 NS073872 (principal investigator), NINDS #R01 NS085136 (principal investigator), and NINDS #R01 NS088257 (principal investigator). He has also received support from the Claire O'Neil Essential Tremor Research Fund (Yale University). S?nchez-Ferro is supported by the Consejer?a de Educaci?n, Juventud y Deporte de la Comunidad de Madrid and the People Programme (Marie Curie Actions) of the European Union?s Seventh Framework Programme (FP7/2007-2013). Serrano is supported by RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (?Rob?tica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV?; S2018/NMT-4331), funded by ?Programas de Actividades I+D en la Comunidad de Madrid? and cofunded by Structural Funds of the European Commission. This research was supported by FEDER funds. Benito-Le?n is supported by the National Institutes of Health, Bethesda, MD, USA (NINDS #R01 NS39422), European Commission (grant ICT-2011-287739, NeuroTREMOR), the Ministry of Economy and Competitiveness (grant RTC-2015-3967-1, NetMD ? platform for the tracking of movement disorder), and the Spanish Health Research Agency (grant FIS PI12/01602 and grant FIS PI16/00451). Romero is supported by the European Commission (grant ICT-2011-287739, NeuroTREMOR) and the Spanish Ministry of Economy and Competitiveness (grant DPI-2015-68664-C4-1-R, NeuroMOD). Louis has received research support from the National Institutes of Health: NINDS #R01 NS094607 (principal investigator), NINDS #R01 NS085136 (principal investigator), NINDS #R01 NS073872 (principal investigator), NINDS #R01 NS085136 (principal investigator), and NINDS #R01 NS088257 (principal investigator). He has also received support from the Claire O'Neil Essential Tremor Research Fund (Yale University). S?nchez-Ferro is supported by the Consejer?a de Educaci?n, Juventud y Deporte de la Comunidad de Madrid and the People Programme (Marie Curie Actions) of the European Union?s Seventh Framework Programme (FP7/2007-2013). Serrano is supported by RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (?Rob?tica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV?; S2018/NMT-4331), funded by ?Programas de Actividades I?+?D en la Comunidad de Madrid? and cofunded by Structural Funds of the European Commission. We acknowledge the neuropsychologist Ver?nica Puertas-Mart?n, for her assistance with the project.
Publisher Copyright:
© 2019 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals, Inc on behalf of American Neurological Association.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
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U2 - 10.1002/acn3.50947
DO - 10.1002/acn3.50947
M3 - Article
C2 - 31769622
AN - SCOPUS:85075738810
SN - 2328-9503
VL - 6
SP - 2531
EP - 2543
JO - Annals of Clinical and Translational Neurology
JF - Annals of Clinical and Translational Neurology
IS - 12
ER -