Development of a method for linear modeling of brain-state and stimulus-related information

Recent studies have reported that non-invasive investigation of functional connectivity by resting state fMRI provides biomarkers for neuropsychiatric diseases. However, most available methods lack sufficient accuracy for diagnosis limiting traditional analysis methods. Here, we propose a method for quantitative estimation of neuronal network states, which dynamically change. Aiming at its clinical application, we hypothesize that spontaneous brain activity at rest reflects the computation of several parallel networks, which can be modeled by their linear combination.