Advanced signal processing algorithms for fMRI

Abstract

It is desirable to have systems which can non-invasively monitor the brain function of patients, especially coma patients, as they cannot communicate with their physicians. The principle of brain modularity states that different regions of the brain perform different functions independently. Thus, spatial independent component analysis (ICA) can be applied on functional magnetic resonance imaging (fMRI) to localise the functions of the brain. Brain signal has long been shown to be nonlinear, so applying a nonlinear ICA method to analyse fMRI signals should yield improved results. However nonlinear ICA yields non-unique solutions, therefore the post-nonlinear (PNL) ICA model has been used instead. Although the results of PNL-ICA were less satisfactory than linear ICA, it is premature to disregard the PNL ICA technique for fMRI signal deconvolution. Further studies need to be conducted on more definitive fMRI data sets before any concrete conclusions can be drawn on the method tested.