TY - JOUR
T1 - Combination of FMRI-SMRI-EEG data improves discrimination of schizophrenia patients by ensemble feature selection
AU - Sui, Jing
AU - Castro, Eduardo
AU - He, Hao
AU - Bridwell, David
AU - Du, Yuhui
AU - Pearlson, Godfrey D.
AU - Jiang, Tianzi
AU - Calhoun, Vince D.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
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U2 - 10.1109/EMBC.2014.6944473
DO - 10.1109/EMBC.2014.6944473
M3 - Article
C2 - 25570841
SN - 1557-170X
VL - 2014
SP - 3889
EP - 3892
JO - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
ER -