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Neuroimage Clin. 2018;20:724-730. doi: 10.1016/j.nicl.2018.09.002. Epub 2018 Sep 4.

Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data.

Author information

1
Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile; Department of Electrical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
2
Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
3
Interdisciplinary Center of Neurosciences, Pontificia Universidad Católica de Chile, Santiago, Chile.
4
Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
5
Brain and Language Lab, Department of Clinical Neuroscience, University of Geneva, Geneva, Switzerland.
6
Interdisciplinary Center of Neurosciences, Pontificia Universidad Católica de Chile, Santiago, Chile; Neurology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile; Neurology, Hospital Dr. Sótero del Río, Santiago, Chile.
7
Interdisciplinary Center of Neurosciences, Pontificia Universidad Católica de Chile, Santiago, Chile; Neurology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
8
Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Department of Psychiatry, Section of Neuroscience, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile; Laboratory for Brain-Machine Interfaces and Neuromodulation, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
9
Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile; Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile. Electronic address: suribe@uc.cl.

Abstract

Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients. To this end, we acquired magnetic resonance imaging data from relapsing-remitting multiple sclerosis patients and healthy subjects. Fractional anisotropy maps, structural and functional connectivity were extracted from the scans. Each of them were used as separate input features to construct support vector machine classifiers. A fourth input feature was created by combining structural and functional connectivity. Patients were divided in two groups according to their degree of disability and, together with the control group, three group pairs were formed for comparison. Twelve separate classifiers were built from the combination of these four input features and three group pairs. The classifiers were able to distinguish between patients and healthy subjects, reaching accuracy levels as high as 89% ± 2%. In contrast, the performance was noticeably lower when comparing the two groups of patients with different levels of disability, reaching levels below 63% ± 5%. The brain regions that contributed the most to the classification were the right occipital, left frontal orbital, medial frontal cortices and lingual gyrus. The developed classifiers based on MRI data were able to distinguish multiple sclerosis patients and healthy subjects reliably. Moreover, the resulting classification models identified brain regions, and functional and structural connections relevant for better understanding of the disease.

KEYWORDS:

Classification; DTI; Multiple sclerosis; Resting state; SVM; fMRI

PMID:
30238916
PMCID:
PMC6148733
DOI:
10.1016/j.nicl.2018.09.002
[Indexed for MEDLINE]
Free PMC Article

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