Sparse dictionary learning for fMRI analysis using autocorrelation maximization

In this paper, the effect of temporal autocorrelations in functional magnetic resonance imaging (fMRI) data on sparse dictionary learning (SDL) is addressed. For sparse general linear model (sGLM), the fMRI time-series is modeled as a linear mixture of several signals such as neural dynamics, structured noise, random noise and unexplained signal variations on the basis of spatial sparseness. These signals are considered as underlying sources and SDL is used to estimate them. However, the sparse GLM model does not take into account the autocorrelations in fMRI data. To address this shortcoming, a new model is proposed to incorporate the prior knowledge about lag-1 autocorrelation into dictionary update stage. This helps improve the sensitivity and specificity of the fMRI data during statistical analysis. Using a simulation study, the effect of the proposed dictionary update on sGLM is compared to conventional sGLM by utilizing various detrending techniques. Furthermore, the proposed update is validated in an sGLM framework for real fMRI datasets, which shows its better capability to estimate neural dynamics in presence of spatiotemporal dependencies.