Estimation of neuronal responses from fMRI data - Havlicek, M; Jan, J; Brazdil, M; Calhoun, V D (Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2011)
In this paper we describe a deconvolution technique for estimation of the neuronal signal from an observed hemodynamic responses in fMRI data. Our approach, based on the Rauch-Tung-Striebel smoother for square-root cubature Kalman filter, enables us to accurately infer the hidden states, parameters, and the input of the dynamic system. Additionally, we enhance the cubature Kalman filter with a variational Bayesian approach for adaptive estimation of the measurement noise covariance.
Keywords: algoritmy; Neurons; magnetická rezonanční tomografie; Signal-To-Noise Ratio; statistika jako téma; Monte Carlo Method; Humans; lidé; Statistics as Topic; Computer Simulation; počítačová simulace; modely neurologické; poměr signál - šum; metoda Monte Carlo; Magnetic Resonance Imaging; neurony; Algorithms; Models, Neurological