Yang Honghui, Liu Jingyu, Sui Jing, Pearlson Godfrey, Calhoun Vince D
Department of Environment Engineering, Northwestern Polytechnical University Xi'an, China.
Front Hum Neurosci. 2010 Oct 25;4:192. doi: 10.3389/fnhum.2010.00192. eCollection 2010.
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.
我们展示了一种混合机器学习方法，用于使用功能磁共振成像（fMRI）和单核苷酸多态性（SNP）数据对精神分裂症患者和健康对照进行分类。该方法包括四个阶段：（1）选择在健康对照和精神分裂症患者之间具有最具区分性信息的SNP，以构建支持向量机集成（SNP-SVME）。（2）选择fMRI图谱中有助于分类的体素，以构建另一个SVME（体素-SVME）。（3）使用独立成分分析（ICA）获得的fMRI激活成分来构建单个支持向量机分类器（ICA-SVMC）。（4）使用多数投票方法将上述三个模型组合成一个单一模块以做出最终决策（联合SNP-fMRI）。该方法通过使用40名受试者（20名患者和20名对照）的完全验证的留一法进行评估。分类准确率为：SNP-SVME为0.74，体素-SVME为0.82，ICA-SVMC为0.83，联合SNP-fMRI为0.87。实验结果表明，将遗传数据和fMRI数据结合起来比单独使用任何一种数据都能获得更好的分类准确率，这表明遗传和脑功能代表了精神分裂症病因学中不同但部分互补的方面。这项研究提出了一种重新评估精神分裂症个体生物学分类的有效方法，这对于识别该疾病的诊断重要标志物也可能是有用的。
