Journal
ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY
Volume 6, Issue 12, Pages 2531-2543Publisher
WILEY
DOI: 10.1002/acn3.50947
Keywords
-
Categories
Funding
- FEDER funds
- National Institutes of Health, Bethesda, MD, USA (NINDS) [R01 NS39422]
- European Commission [ICT-2011-287739]
- Ministry of Economy and Competitiveness [RTC-2015-3967-1]
- Spanish Health Research Agency [FIS PI12/01602, FIS PI16/00451]
- Spanish Ministry of Economy and Competitiveness [DPI-2015-68664-C41-R]
- National Institutes of Health: NINDS [R01 NS094607, R01 NS085136, R01 NS073872, R01 NS088257]
- Claire O'Neil Essential Tremor Research Fund (Yale University)
- Consejeria de Educacion, Juventud y Deporte de la Comunidad de Madrid
- People Programme (Marie Curie Actions) of the European Union
- RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV) - Programas de Actividades I+D en la Comunidad de Madrid [S2018/NMT-4331]
- European Commission
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available