Sui Jing, Castro Eduardo, He Hao, Bridwell David, Du Yuhui, Pearlson Godfrey D, Jiang Tianzi, Calhoun Vince D
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3889-92. doi: 10.1109/EMBC.2014.6944473.
Multimodal brain imaging data fusion is a scientifically interesting and clinically important topic; however, there is relatively little work on N-way data fusion. In this paper, we applied multi-set canonical correlation analysis (MCCA) to combine data of resting state fMRI, EEG and sMRI, in order to elucidate the abnormalities that underlie schizophrenia patients and also covary across multiple modalities. We also tested whether the identified group-discriminative components can be used for feature selection in group classification. MCCA is demonstrated to be an effective feature selection technique, especially in multimodal fusion. We also proposed an ensemble feature selection scheme by combining two sample t-test, MCCA and support vector machine with recursive feature elimination (SVM-RFE), resulting in optimal group-discriminating features for each modality. Finally, we compared the classifying power between two groups based on the above selected features via 7 modality-combinations. Results show that the fMRI-sMRI-EEG combination derives the best classification accuracy in training (91%) and predication rate (100%) in testing data, validating the effectiveness and advantages of multimodal fusion in discriminating schizophrenia.
多模态脑成像数据融合是一个具有科学趣味性和临床重要性的课题；然而，关于N路数据融合的研究相对较少。在本文中，我们应用多集典型相关分析（MCCA）来结合静息态功能磁共振成像（fMRI）、脑电图（EEG）和结构磁共振成像（sMRI）的数据，以阐明精神分裂症患者潜在的异常情况，这些异常情况在多种模态中也存在共变关系。我们还测试了所识别的组间判别成分是否可用于组分类中的特征选择。事实证明，MCCA是一种有效的特征选择技术，尤其是在多模态融合中。我们还提出了一种集成特征选择方案，将双样本t检验、MCCA和带递归特征消除的支持向量机（SVM-RFE）相结合，从而为每种模态生成最优的组间判别特征。最后，我们基于上述选定特征，通过7种模态组合比较了两组之间的分类能力。结果表明，fMRI-sMRI-EEG组合在训练数据中获得了最佳分类准确率（91%），在测试数据中获得了最佳预测率（100%），验证了多模态融合在鉴别精神分裂症方面的有效性和优势。
