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To explain individual differences in development, behavior, and cognition,most previous studies focused on projecting resting-state functional MRI (fMRI)based functional connectivity (FC) data into a low-dimensional space via lineardimensionality reduction techniques, followed by executing analysis operations.However, linear dimensionality analysis techniques may fail to capturenonlinearity of brain neuroactivity. Moreover, besides resting-state FC, FCbased on task fMRI can be expected to provide complementary information.Motivated by these considerations, we nonlinearly fuse resting-state andtask-based FC networks (FCNs) to seek a better representation in this paper. Wepropose a framework based on alternating diffusion map (ADM), which extractsgeometry-preserving low-dimensional embeddings that successfully parameterizethe intrinsic variables driving the phenomenon of interest. Specifically, wefirst separately build resting-state and task-based FCNs by symmetric positivedefinite matrices using sparse inverse covariance estimation for each subject,and then utilize the ADM to fuse them in order to extract significantlow-dimensional embeddings, which are used as fingerprints to identifyindividuals. The proposed framework is validated on the PhiladelphiaNeurodevelopmental Cohort data, where we conduct extensive experimental studyon resting-state and fractal $n$-back task fMRI for the classification ofintelligence quotient (IQ). The fusion of resting-state and $n$-back task fMRIby the proposed framework achieves better classification accuracy than anysingle fMRI, and the proposed framework is shown to outperform several otherdata fusion methods. To our knowledge, this paper is the first to demonstrate asuccessful extension of the ADM to fuse resting-state and task-based fMRI datafor accurate prediction of IQ.

Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction