Recent advances in the data analysis method of functional magnetic resonance imaging and its applications in neuroimaging

Functional magnetic resonance imaging (fMRI ) has opened a new area to explore the human brain. The fMRL can reveal the deep insights of spatial and temporal changes underlying a broad range of brain function, such as motor, vision, memory and emotion, all of which are helpful in the clinical investigation. In this paper, we introduce some recent-developed algorithms for fMRI signal detection such as model-driven method (general linear model, deconvolution model, non-linear model, etc. ) and datt-driven method (principle component analysis, independent component analysis, self-organization mapping, clustered constrained non-negative matrix factorization, etc.). We also propose several important applications of neuroimaging and point out their shortcomings and future perspectives.