A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease
A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease
复制标题
基于多级 ROI 特征的机器学习方法，用于检测帕金森病的形态生物标志物
DOI：
10.1016/j.neulet.2017.04.034
复制
发表时间：
2017-06-09
影响因子：
2.5
通讯作者：
Dai, Yakang
中科院分区：
文献类型：
--
作者：
Peng, Bo;Wang, Suhong;Dai, Yakang
近年来，机器学习方法已被广泛用于检测感兴趣区域（ROI）中的神经成像生物标志物并辅助诊断神经退行性疾病。本研究的创新之处在于采用基于多层次感兴趣区域特征的机器学习方法来检测帕金森病（PD）中敏感的形态学生物标志物。具体地，低级别ROI特征（灰质体积、皮质厚度等）并将高层相关特征（ROI之间的连通性）整合在一起，构建多级ROI特征。在分类算法中采用了基于滤波器和包装器的特征选择方法和多核支持向量机。研究中包括来自帕金森氏进展标志物倡议（PPMI）数据集的69名PD患者和103名正常对照的T1加权脑磁共振（MR）图像。机器学习方法在PD患者和正常对照之间的分类中表现良好，准确率为85.78%，特异性为87.79%，灵敏度为87.64%。PD患者与正常对照组比较，最敏感的生物标志物主要分布在额叶、父母叶、边缘叶、颞叶和中央区。与其他使用单级特征的分类方法相比，我们的方法与多级ROI特征的分类性能显着提高。所提出的方法显示出有前途的识别能力，检测PD的形态学生物标志物，从而证实了我们的方法在辅助诊断疾病的潜力。(C)2017爱思唯尔B.V.保留所有权利。
Machine 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.