A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia.
A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia.
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DOI：
10.3389/fnhum.2010.00192
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发表时间：
2010
影响因子：
2.9
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Yang H;Liu J;Sui J;Pearlson G;Calhoun VD
我们使用功能磁共振成像(FMRI)和单核苷酸多态性(SNP)数据，展示了一种混合机器学习方法来对精神分裂症患者和健康对照组进行分类。该方法分为四个阶段：(1)选择健康人和精神分裂症患者之间具有最大区分信息的SNPs构建支持向量机集成(SNP-SVME)。(2)选取fMRI图中对分类有贡献的体素，构建另一个SVME(Voxel-SVME)。(3)将独立成分分析(ICA)得到的fMRI激活成分用于构造单个支持向量机分类器(ICA-SVMC)。(4)采用多数投票方法将上述三个模型组合成一个模块进行最终决策(组合SNP-fMRI)。该方法由40名受试者(20名患者和20名对照)采用完全有效的留一法进行评估。SNP-SVME、Voxel-SVME、ICA-SVMC和联合SNP-fMRI的分类准确率分别为0.74、0.82、0.83和0.87。实验结果表明，联合使用遗传学和fMRI数据比单独使用两者都能获得更好的分类精度，这表明遗传和脑功能代表了精神分裂症病因学的不同方面，但部分互补。这项研究建议了一种有效的方法来重新评估精神分裂症患者的生物学分类，这也可能有助于确定该疾病的诊断重要标志物。
We demonstrate a hybrid machine learning method to classify schizophrenia patients and healthy controls, using functional magnetic resonance imaging (fMRI) and single nucleotide polymorphism (SNP) data. The method consists of four stages: (1) SNPs with the most discriminating information between the healthy controls and schizophrenia patients are selected to construct a support vector machine ensemble (SNP-SVME). (2) Voxels in the fMRI map contributing to classification are selected to build another SVME (Voxel-SVME). (3) Components of fMRI activation obtained with independent component analysis (ICA) are used to construct a single SVM classifier (ICA-SVMC). (4) The above three models are combined into a single module using a majority voting approach to make a final decision (Combined SNP-fMRI). The method was evaluated by a fully validated leave-one-out method using 40 subjects (20 patients and 20 controls). The classification accuracy was: 0.74 for SNP-SVME, 0.82 for Voxel-SVME, 0.83 for ICA-SVMC, and 0.87 for Combined SNP-fMRI. Experimental results show that better classification accuracy was achieved by combining genetic and fMRI data than using either alone, indicating that genetic and brain function representing different, but partially complementary aspects, of schizophrenia etiopathology. This study suggests an effective way to reassess biological classification of individuals with schizophrenia, which is also potentially useful for identifying diagnostically important markers for the disorder.
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DOI：
10.1101/sqb.2003.68.69
发表时间：
2003-01-01
期刊：
COLD SPRING HARBOR SYMPOSIA ON QUANTITATIVE BIOLOGY
影响因子：
--
作者：
Fan, JB;Oliphant, A;Chee, MS
通讯作者：
Chee, MS
影响因子：
9.8
作者：
Gurling, HMD;Kalsi, G;Curtis, D
通讯作者：
Curtis, D
影响因子：
5.7
作者：
Demirci, Oguz;Clark, Vincent P.;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
0.9
作者：
Arai, Shinsaku;Shibata, Hiroki;Fukumaki, Yasuyuki
通讯作者：
Fukumaki, Yasuyuki
影响因子：
5.7
作者：
Calhoun VD;Liu J;Adali T
通讯作者：
Adali T