OBJECTIVE: To understand the association betweenbrain networks and behaviors of an individual, most studiesbuild predictive models based on functional connectivity (FC) from a single dataset with linear analysis techniques. Such approaches may fail to capture the nonlinear structure of brainnetworks and neglect the complementary information contained in FC networks (FCNs) from multiple datasets. To address this challenging issue, we use multiview dimensionality reduction to extract a coherent low-dimensional representation of the FCNs from resting-state and emotion identification task-based functional magnetic resonance imaging (fMRI) datasets. METHODS: We propose a scheme based on multiview diffusion map to extract intrinsic features while preserving the underlying geometric structure of high dimensional datasets. This method is robust to noise and small variations in the data. RESULTS: After validation on the Philadelphia Neurodevelopmental Cohort data, the predictive model built from both resting-state and emotion identification task-based fMRI datasets outperforms the one using each individual fMRI dataset. In addition, the proposed model achieves better prediction performance than principal component analysis (PCA) and three other competingdata fusion methods. CONCLUSION AND SIGNIFICANCE: Our framework for combing multiple FCNs in one predictive model exhibits improved prediction performance. To our knowledge, we demonstrate a first application of multiview diffusion map to successfully fuse differenttypes of fMRI data for fluid intelligence (gF).
OBJECTIVE: To understand the association betweenbrain networks and behaviors of an individual, most studiesbuild predictive models based on functional connectivity (FC) from a single dataset with linear analysis techniques. Such approaches may fail to capture the nonlinear structure of brainnetworks and neglect the complementary information contained in FC networks (FCNs) from multiple datasets. To address this challenging issue, we use multiview dimensionality reduction to extract a coherent low-dimensional representation of the FCNs from resting-state and emotion identification task-based functional magnetic resonance imaging (fMRI) datasets. METHODS: We propose a scheme based on multiview diffusion map to extract intrinsic features while preserving the underlying geometric structure of high dimensional datasets. This method is robust to noise and small variations in the data. RESULTS: After validation on the Philadelphia Neurodevelopmental Cohort data, the predictive model built from both resting-state and emotion identification task-based fMRI datasets outperforms the one using each individual fMRI dataset. In addition, the proposed model achieves better prediction performance than principal component analysis (PCA) and three other competingdata fusion methods. CONCLUSION AND SIGNIFICANCE: Our framework for combing multiple FCNs in one predictive model exhibits improved prediction performance. To our knowledge, we demonstrate a first application of multiview diffusion map to successfully fuse differenttypes of fMRI data for fluid intelligence (gF).