Discriminative Analysis of Migraine without Aura: Using Functional and Structural MRI with a Multi-Feature Classification Approach.
Magnetic resonance imaging (MRI) is by nature a multi-modality technique that provides complementary information about different aspects of diseases. So far no attempts have been reported to assess the potential of multi-modal MRI in discriminating individuals with and without migraine, so in this study, we proposed a classification approach to examine whether or not the integration of multiple MRI features could improve the classification performance between migraine patients without aura (MWoA) and healthy controls. Twenty-one MWoA patients and 28 healthy controls participated in this study. Resting-state functional MRI data was acquired to derive three functional measures: the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength; and structural MRI data was obtained to measure the regional gray matter volume. For each measure, the values of 116 pre-defined regions of interest were extracted as classification features. Features were first selected and combined by a multi-kernel strategy; then a support vector machine classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using a leave-one-out cross-validation method, and the final classification accuracy obtained was 83.67% (with a sensitivity of 92.86% and a specificity of 71.43%). The anterior cingulate cortex, prefrontal cortex, orbitofrontal cortex and the insula contributed the most discriminative features. In general, our proposed framework shows a promising classification capability for MWoA by integrating information from multiple MRI features.
Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu, Sichuan, China.
Qiongmin
Q
Zhang
Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.
Qizhu
Q
Wu
Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu, Sichuan, China.|Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
Junran
J
Zhang
Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu, Sichuan, China.
Ling
L
He
Computer and Information Engineering School, Guangxi Teachers Educational University, Nanning, Guangxi, China.
Jiangtao
J
Huang
Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu, Sichuan, China.
Jiang
J
Zhang
Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu, Sichuan, China.
Hua
H
Huang
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
Qiyong
Q
Gong
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