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A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease
Peng, B.; S. H. Wang; Z. Y. Zhou; Y. Liu; B. T. Tong; T. Zhang and Y. K. Dai
2017
Source PublicationNeuroscience Letters
Volume651
AbstractMachine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper-based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. Tl-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. (C) 2017 Elsevier B.V. All rights reserved.
Indexed Bysci ; ei
Language英语
Document Type期刊论文
Identifierhttp://ir.ciomp.ac.cn/handle/181722/59151
Collection中科院长春光机所知识产出
Recommended Citation
GB/T 7714
Peng, B.,S. H. Wang,Z. Y. Zhou,et al. A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease[J]. Neuroscience Letters,2017,651.
APA Peng, B.,S. H. Wang,Z. Y. Zhou,Y. Liu,B. T. Tong,&T. Zhang and Y. K. Dai.(2017).A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.Neuroscience Letters,651.
MLA Peng, B.,et al."A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease".Neuroscience Letters 651(2017).
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