Andrea Gabrielli

This paper uses reservoir computing with noisy linear recurrent neural networks to create digital twins of brain activity from resting-state fMRI data, enabling prediction and decomposition of BOLD signals into low-dimensional latent states.

This study uses partial correlation on fMRI data to better align functional connectivity with structural connectivity, showing improved matches with regularization and thresholding.