Neural network modeling of neuronal-vascular coupling

Abstract

Sensory or cognitive stimuli in functional MRI (fMRI) experiments activate neuronal populations in specific areas of the brain. Neuronal events in activated brain regions cause changes of blood flow and blood oxygenation level. FMRI signals are sensitive to hemodynamic events ensuing neuronal activation in the brain. The authors use a neural network to model neuronal-vascular coupling of human brain with images obtained in fMRI experiments. The nonlinear mappings modeled by training a network were used to approximate time series acquired in language comprehension and visual experiments. The models of neuronal-vascular coupling realized using the neural network were better than those rendered by a linear system model.