Predict the slow oscillation of the single-vessel resting-state fMRI signal of rats and humans with echo state networks

Single-vessel fMRI has enabled the detection of slow fluctuations (<0.1Hz) of the hemodynamic fMRI signal from individual vessels in both rat and human brains. The Echo State Network (ESN) has been used to encode the slowly changing temporal dynamics of individual vessels by training the network to predict the oscillatory signals from individual vessels 10 seconds ahead in time. Distinct network reservoirs are optimized for human and animal vascular signals, showing high correlation for the ESN-predictive signal with the original fresh data. This work establishes ESN-based signal prediction for the slow-oscillatory brain fMRI signal in real-time.