Fusion of multimodal brain connectivity networks for IQ prediction with alternative diffusion map скачать в хорошем качестве

Fusion of multimodal brain connectivity networks for IQ prediction with alternative diffusion map

Speaker: Yu-Ping Wang, Dept. of Biomedical Engineering, Tulane University, USA Abstract: Functional connectivity (FC) has been used to study individual differences in development, behavior, and cognition. However, current approaches are mainly using linear dimensionality analysis for extracting essential network patterns, which fail to capture the nonlinearity of the brain network. Herein, we propose a framework based on alternating diffusion map (ADM) to extract geometry-preserving low-dimensional embeddings. Specifically, we first separately build resting-state and task-based FC networks by symmetric positive definite matrices using sparse inverse covariance estimation for each subject, and then utilize the ADM for their fusion. Finally, the low-dimensional embeddings are extracted as fingerprints to identify individuals. The proposed framework is validated on the Philadelphia Neurodevelopmental Cohort data for the classification of intelligence quotient (IQ). The fusion of resting-state and n-back task fMRI by the proposed framework achieves better classification accuracy than any single fMRI, and the proposed framework is shown to outperform several other data fusion methods.