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Abstract:
Previously we developed an echo-state network (ESN) to predict the future temporal evolution of the rs-fMRI slow oscillatory feature from both rodent and human brains. In particular, rs-fMRI signals from individual blood vessels that were strongly correlated with neural calcium oscillations were used to train an ESN to predict brain state-specific rs-fMRI signal fluctuations. Here, the ESN-based predictive model was applied to classify rs-fMRI datasets from the Human Connectome Project (HCP). The ESN enables to decouple the brain state-dependent global rs-fMRI signal fluctuation from the intrinsic activity of the default-mode network.
