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Machine learning models for diagnosis of essential tremor and dystonic tremor using grey matter morphological networks.
- Source :
-
Parkinsonism & related disorders [Parkinsonism Relat Disord] 2024 Jul; Vol. 124, pp. 106985. Date of Electronic Publication: 2024 Apr 28. - Publication Year :
- 2024
-
Abstract
- Background: Essential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain grey matter (GM) morphological networks and combine those with machine learning models.<br />Methods: 3D-T1 structural images of 75 ET patients, 71 DT patients, and 79 healthy controls (HCs) were acquired. We used voxel-based morphometry to obtain GM images and constructed GM morphological networks based on the Kullback-Leibler divergence-based similarity (KLS) method. We used the GM volumes, morphological relations, and global topological properties of GM-KLS morphological networks as input features. We employed three classifiers to perform the classification tasks. Moreover, we conducted correlation analysis between discriminative features and clinical characteristics.<br />Results: 16 morphological relations features and 1 global topological metric were identified as the discriminative features, and mainly involved the cerebello-thalamo-cortical circuits and the basal ganglia area. The Random Forest (RF) classifier achieved the best classification performance in the three-classification task, achieving a mean accuracy (mACC) of 78.7%, and was subsequently used for binary classification tasks. Specifically, the RF classifier demonstrated strong classification performance in distinguishing ET vs. HCs, ET vs. DT, and DT vs. HCs, with mACCs of 83.0 %, 95.2 %, and 89.3 %, respectively. Correlation analysis demonstrated that four discriminative features were significantly associated with the clinical characteristics.<br />Conclusion: This study offers new insights into the structural network mechanisms of ET and DT. It demonstrates the effectiveness of combining GM-KLS morphological networks with machine learning models in distinguishing between ET, DT, and HCs.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2024. Published by Elsevier Ltd.)
- Subjects :
- Humans
Male
Female
Middle Aged
Aged
Dystonic Disorders diagnostic imaging
Dystonic Disorders pathology
Dystonic Disorders diagnosis
Nerve Net diagnostic imaging
Nerve Net pathology
Tremor diagnostic imaging
Tremor diagnosis
Tremor pathology
Adult
Machine Learning
Essential Tremor diagnostic imaging
Essential Tremor pathology
Gray Matter diagnostic imaging
Gray Matter pathology
Magnetic Resonance Imaging
Subjects
Details
- Language :
- English
- ISSN :
- 1873-5126
- Volume :
- 124
- Database :
- MEDLINE
- Journal :
- Parkinsonism & related disorders
- Publication Type :
- Academic Journal
- Accession number :
- 38718478
- Full Text :
- https://doi.org/10.1016/j.parkreldis.2024.106985